Journal articles on the topic 'Telugu Script'

Telugu language can be mentioned next to tamil in terms of antiquity and pronuncication. Telugu is spoken in andhra pradesh and place like south africa in our indian country telugu is the second most widely spoken language after hindi. It is a literary language like tamil. Those who study the reson for the name o this language will think in various ways that the word thrillingam was changed to telugu and it is also believed that the word ‘Tenuge’ which meens sweetness was given knowing that it was telugu. Many grammars have been written in the language from the western country. Northern Scholars, such as nannayapattar have written a number of literatures in this language. The language, which is spoken by more people than tamil is thriving with a variety of creativity. In terms of gender discrimination, telugu can be seen to be different from other dravidian languages the sound and writing script of the telugu language are considered to be the most excellent. The people of chennai who migrated to places like sumatra and java are considered as telugus. That is why the telugu language is still existing as very prominent today.

Recognition of Indian language scripts is a challenging problem. Work for the development of complete OCR systems for Indian language scripts is still in infancy. Complete OCR systems have recently been developed for Devanagri and Bangla scripts. Research in the field of recognition of Telugu script faces major problems mainly related to the touching and overlapping of characters. Segmentation of touching Telugu characters is a difficult task for recognizing individual characters. In this paper, the proposed algorithm is for the segmentation of touching Hand written Telugu characters. The proposed method using Drop-fall algorithm is based on the moving of a marble on either side of the touching characters for selection of the point from where the cutting of the fused components should take place. This method improvers the segmentation accuracy higher than the existing one.

In the field of computer vision and document analysis, the identification and categorization of Indian scripts in natural scene images pose a difficult yet crucial challenge. The variety of characters and intricate writing styles in Indian scripts require reliable solutions for precise identification under different environmental conditions. This study presents a novel CNN model designed for identifying scripts in Indian multilingual document images captured by cameras. Experimental evaluations of the model's performance were conducted with two regional languages (Odia and Telugu) and one national language (Hindi). The average accuracy in script recognition for the three language combinations is 95.66%, with Odia achieving 99.00%, Hindi 90.33%, and Telugu 98.12%. The model achieved the highest accuracy in recognition. The model achieved the highest accuracy in recognition Keywords: Text Recognition, Image Augmentation, CNN, LSTM, VGG, ResNet, DenseNet, Datasets, Natural Images

The growing demand for automated evaluation systems in educational environments, especially for languages with complex scripts like Telugu, drives the motivation for this research. Traditional handwriting recognition methods for Telugu have faced challenges with limited accuracy and adaptability, particularly in real-world educational scenarios. These limitations often result in reduced precision in character and sentence recognition, along with increased processing delays. This study proposes a novel system for the automated evaluation of handwritten Telugu answer scripts. The model incorporates advanced preprocessing techniques such as adaptive thresholding for binarization and Gaussian blurring for noise reduction, enhancing the readability of diverse handwriting styles. Robust feature extraction is achieved using Convolutional Neural Networks (CNNs) like ResNet101 and Inception networks. To capture the contextual flow of Telugu scripts, Quad Long Short-Term Memory (LSTM) networks are utilized, with Attention Mechanisms improving focus on intricate character sequences. Additionally, Transformer-based models like BERT, trained on Telugu text, enable the system to better understand the syntax and semantics of the language. For evaluation, Visual BERT embeddings and cosine similarity metrics are employed to ensure precise semantic analysis and answer matching. Testing across multiple datasets demonstrates a significant improvement over existing approaches, with higher precision and accuracy in character recognition and notable enhancements in area under the curve (AUC). Sentence recognition also shows marked improvements in precision, accuracy, and AUC. This work represents a significant advancement in automated evaluation systems for languages with intricate scripts, improving both efficiency and accuracy in educational assessments. Beyond its primary application, the system offers potential for broader uses in document processing and language technologies. This research makes a valuable contribution to the fields of automated handwriting recognition and natural language processing for Indic scripts.

Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification.

Behaviour reputation stands as one of the earliest applications in sample reputation. While spotting handwritten characters is an clean venture for humans, it is a formidable task for computer structures. Optical Character Recognition (OCR) is an crucial answer primarily based on optical systems, which enables automatic reputation of scanned and digitized characters This paper explores into optical man or woman popularity strategies in particular developed for handwriting Telugu within the characters. Telugu, a Dravidian language spoken especially in Andhra Pradesh and Telangana, India, offers precise challenges because of its complex alphabet Basic parts of Telugu script together with "vattu" which stands for vowels and "gunitalu" which means that tone the complicated syllables add to the complexity. Combining OCR strategies with Harris corner popularity, the paper affords insights into the accuracy and efficiency of handwritten Telugu person reputation and the fidelity of handwriting This have a look at contributes to the development of character reputation in particular on in complex written languages ​​which include Telugu and gives realistic explanations for handwriting verification processing. Key Words: Optical Character Recognition (OCR), Telugu, Vattu, Gunitalu, Harris corner detection, Handwritten Character Recognition, Signature Verification.

In handwritten document images, segmenting text lines is a very challenging task due to various reasons like variability in intra baseline skew and inter line distance between text lines. So far, no work is reported in the literature for the straightening of handwritten Telugu languages. Telugu is one of the most popular languages of India that is spoken by more than 80 million people especially in South India. Telugu characters are mostly compound characters and that is way the straightening task of Telugu document is more challenging tasks than European languages. This paper introduces a novel approach for segmenting and straightening text lines of handwritten Telugu documents based on smearing and regression approach (SRA). This method initially performs preprocessing and estimates parameters by dividing into connected components of Telugu script. A horizontal and vertical run length-smearing algorithm is used in this paper to shape text lines. To identify text lines more precisely cubic polynomial regression is used between vertical midpoints of two blocks of compound handwritten Telugu characters. A simple logic is derived on this to achieve final process. We tested the proposed algorithm with three different kind of 1000 handwritten documents. The performance of proposed method is evaluated by using matchScore, detection rate, recognition accuracy and F-measure. The experimental results indicates the efficiency of the proposed method over the existing methods.

In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.

Abstract: Sign language is the basic communication method among hearing disabled and speech disabled people. To express themselves, they require an interpreter or motion sensing devices who/which converts sign language in a few of the standard languages. However, there is no system for those who speak in the Telugu language and hence they are forced to speak in the national language over the regional language of their culture along with the same issues of cumbersome hardware or need for an interpreter. This paper proposes a system that detects hand gestures and signs from a real-time video stream that is processed with the help of computer vision and classified with object detection YOLOv3 algorithm. Additionally, the labels are mapped to corresponding Telugu text. The style of learning is transfer learning, unlike conventional CNNs, RNNs or traditional Machine Learning models. It involves applying a pre-trained model onto a completely new problem to solve the related problem statement and adapts to the new problem’s requirements efficiently. This requires lesser training effort in terms of dataset size and greater accuracy. It is the first system developed as a sign language translator for Telugu script. It has given the best results as compared to the existing systems. The system is trained on 52 Telugu letters, 10 numbers and 8 frequently used Telugu words.

This paper examines the dynamic interplay between Shakespearean works and early modern Telugu literature from 1870 to 1905, highlighting a robust intercultural dialogue that shaped literary innovation rather than mere imitation. This is a fascinating convergence where global literary icons met a local genius and rewrote the script of cultural exchange. Rather than passive borrowing, Telugu poets and writers like Kandukuri Veereshalingam, Vavilala Vasudeva Sastri, Duvvuri Ramireddy, and Mullapudi Venkata Ramana actively reimagined Shakespeare&rsquo;s plays, infusing them with indigenous cultural values, poetic forms, and socio-political critique. These adaptations transformed universal themes of love, power, jealousy, and existential conflict into vehicles for social reform, gender discourse, and anti-colonial nationalism within Telugu society. This study uses contemporary translation studies and postcolonial theory to reveal how these creative reworkings challenged colonial narratives, expanded the Telugu literary canon, and carved out a unique path to modernity. The Shakespearean legacy here is not a one-way street but a transcultural negotiation, where Telugu literature did not just echo the Bard; it conversed, contested, and innovated. This rich intercultural exchange exemplifies how translation can be a bold act of cultural appropriation and transformation, proving that even centuries-old global classics can spark fresh, localized voices and futures. &nbsp; <strong>Keywords</strong><em>:</em><strong> </strong>Translation Studies; Postcolonial Theory; Cultural Adaptation;&nbsp; Transculturation; Social Reform

Secure authentication is a vital component for device security. The most basic form of authentication is by using passwords. With the evolution of smart devices, selecting stronger and unbreakable passwords have become a challenging task. Such passwords if written in native languages tend to offer improved security since attackers having no knowledge of such scripts finding it hard to crack. This article proposes two zone-wise feature extraction approaches - zone-wise structural and directional (ZSD) and zone-wise slopes of dominant points (ZSDP), to recognize online handwritten script and word in four major Indic scripts - Devanagari, Bengali, Telugu and Tamil. These features have been used separately and in combination in HMM-based platform for recognition purpose. The dimension reduction of the ZSD-ZSDP combination with factor analysis has shown the best performance in all the four scripts. This work can be utilized for setting up the authentication schemes with the Indic scripts' passwords thus rendering it difficult to crack by hackers having no knowledge of such scripts.

For grammar checking of&nbsp; complex sentences, it is necessary to identify the structure of these sentences. The structure of complex sentences can be identified on the basis of number of clauses and types of clauses present in them. If a sentence contains a dependent clause along with independent clause then it is a complex sentence. Once the sentence is identified as complex sentence, the next step is to identify its pattern. After identification of patterns, various clauses present in the sentence are extracted and grammar checking is performed on them. A grammar checking system for complex sentences of Telugu language has been done with grammatical error detection and correction. This research work on grammar checking of complex sentences is based on the assumption that the input sentences will be in Telugu script.

Optical Character Recognition (OCR) is a technique that converts printed text and images into a digitized form, which can be manipulated by a machine. It has many application sectors like Banking, Financial, Legal applications, etc. Initially researchers were addressed and proposed many algorithms in image processing for character recognition and mapping. Most of the researchers focused on the Latin script English as it was supported by the Encoding standard ASCII. Later, people start realizing that OCR techniques for other languages are also gaining momentum these days. With the advent of technology and Unicode revolution, native language-based OCR solutions started emerging. In this paper, we aim to focus on the latest machine learning techniques applied on OCR for the language English and two languages from Indian continent were presented. Out of the two Indian languages, one is the stroke-based language, i.e. Hindi, and the other being cursive script-based language Telugu.

This paper presents the influence of Dravidian language and its acquired characteristics on the secondary language. In this paper the three Dravidian languages, namely, Tamil, Malayalam and Telugu were considered for studying the regional language features on English language (secondary language). The present study has been conducted by taking handwriting exemplar of 565 subjects and focus has been made on the Handwriting features of secondary language due to primary language. The main objective of the work is to observe what effect, if any, between two well- known scripts of the Handwriting of the same subject is carrying. The observations of this paper have shown that writer carrying the individual characters from the primary language while writing the secondary language. Handwriting and its uniqueness are a result of our subconscious mind and the same is observed in this study where the writer has picked up his or her frequently used individual characters from his or her primary language into the English script. This study will be useful in proving the authorship of questioned document where only one of the handwriting samples is available (i.e., either Regional or English) and the other handwriting&rsquo;s authorship needs to be identified.

This work explores the problem of news headline classification in Natural Language Processing (NLP). This is a widely studied topic in the realm of NLP. However, limited work has been done on multilingual text classification (specific to Indic languages). Indic language models focus primarily on widely spoken Indian Languages. The performance of these multilingual language models is measured on the Indic News Headline dataset (iNLTK). This dataset also serves as a genre classification dataset (containing ten different genres/categories of headlines). This is done for languages such as Gujarati, Malayalam, Marathi, Tamil, and Telugu (non-Latin scripts). Indic languages are sometimes challenging as the data may contain English (Latin script) mixed with non-Latin script. The performance of recently released models such as Saravam-1 (an LLM launched by Saravam AI) is compared to traditional approaches (BERT-like models) such as DistilBERT, XLMRoBERTa, and IndicBERT. The Saravam-1 LLM model is fine-tuned using the PEFT LoRA approach. The performance is then compared using weighted precision, recall, and F1 scores with the fine-tuned version of the other three BERT-like models. The performance of Saravam-1 LLM stands out from other BERT models with a weighted F1 score of 0.87 on the test set. The other three fine-tuned models, XLMRoBERTa, DistilBERT, and IndicBERT, still perform reasonably with weighted F1 scores of 0.84, 0.82, and 0.79.

Abstract The Vaidyaciṃtāmaṇi, authored by Vallabhāācārya or Vallabhendra, holds great significance as a treatise of Ayurveda, particularly within Telugu-speaking states on South India. This comprehensive work encompasses all the essential aspects for preventive healthcare as well as management of diseases through Ayurveda. It is a Sanskrit text with the script in Telugu and the Shloka in Sanskrit. The Vaidyachintamani manuscript originated in the 15th century A.D. in various manuscripts located in different places. Toward the end of the nineteenth century, the text was published in Telugu. The aim of this work is to analyze the impact of Vaidyaciṃtāmaṇi on South Indian Ayurveda and its contributions to therapeutic methodologies. The compendium of Vaidyaciṃtāmaṇi is meticulously structured into 26 Vilasas (sections), each further subdivided into 73 Prakaranas (chapters) corresponding to specific topics. It provides comprehensive information on diverse Ayurvedic dosage forms tailored to individual diseases. The compendium extensively delineates etiological factors, pathology, diagnostic features, Nadi Pariksha (pulse examination), and Asthastana Pariksha (examination of eight things that is, pulse, touch, appearance, auscultation, eyes, feces, urine, and tongue). The concluding section encompasses Rasa, Maharasa, Dhatu, Ratna, and Vishadravya, along with their Shodhana and Marana procedures under Suddhi Prakarana. Additionally, it includes detailed expositions on different Yantras, Mana (weights and measurements), Paribhasha Prakarana (chapter on technical terminology), and Visha Chikitsa (treatments of poison). Notably, Vaidyaciṃtāmaṇi is listed in the authoritative Drugs and Cosmetics Act 1940 book list. Moreover, formulations attributed to Vaidyaciṃtāmaṇi are incorporated in various pharmacopeias, formularies, and therapeutic indices. Consequently, this publication is considered to be a flawless rendition.

In the mids of cold northern winds and landscape covered with snow we are pleased to announce the first ALA issue of the year 2019, which contains six research articles. Warm congratulation goes to all the authors, and words of appreciation to the Editorial team and recently enlarged proofreading team that have been working very hard in order to offer state-of-the-art contemporary linguistic research in this journal.&#x0D; The present issue is opened up by Mayuri J. DILIP and Rajesh KUMAR, who present a unified account of licensing conditions of Negative Polarity Items (NPI) in Telugu. In their work “Negative Polarity Items in Telugu” they analyze the distribution of NPIs in complex sentences with embedded clauses, and conclude that negation c-commanding NPI be conducted at the base-generated position.&#x0D; Kun SUN with his article “The Integration Functions of Topic Chains in Chinese Discourse” thoroughly presents the long and extensive Chinese research tradition on topic chains, and re-examines their core characteristics with the help of the so-called “integration functions”.&#x0D; The following paper “Tracing the Identity and Ascertaining the Nature of Brahmi-derived Devanagari Script” by Krishna Kumar PANDEY and Smita JHA exploits the orthographic design of Brahmi-derived scripts. Authors argue that such scripts should not be described with the existing linguistic properties of alphabetic and syllabic scripts but should instead gains its own categorization with a unique descriptor.&#x0D; Chikako SHIGEMORI BUČAR successfully submitted the article “Image of Japan among Slovenes” in which she represents the process and mechanism of borrowing from Japanese into Slovene. Conclusions briefly touch the image of Japan seen through the borrowing process and consolidated loanwords, and predict possible development of borrowing in the near future.&#x0D; Another interesting paper “Understanding Sarcastic Metaphorical Expression in Hindi through Conceptual Integration Theory” was authored by Sandeep Kumar SHARMA and Sweta SINHA. Based on a corpus of five thousand sentences, authors examine the abstract notion of sarcasm within the framework of conceptual integration theory, and with special reference to Hindi language. Findings aim to provide a theoretical understanding on how Hindi sarcasm is perceived among the native speakers.&#x0D; And last but not least, Điệp Thi Nhu NGUYỄN, An-Vinh LƯƠNG, and Điền ĐINH humbly observe research backlog in the area of Vietnamese text readability and write their paper “Affection of the part of speech elements in Vietnamese text readability” to encourage researchers to further explore the field and put Vietnamese findings on the world’s map.&#x0D; Editors and Editorial Board wish the regular and new readers of the ALA journal a pleasant read full of inspiration.

&lt;table width="593" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="387"&gt;&lt;p&gt;Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e., text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

&lt;table width="593" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="387"&gt;&lt;p&gt;Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e., text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

Abstract Inscriptions engraved on surfaces such as stone and copper plates provide insights into governance, societal customs, and the practice of Ayurveda from ancient times to the present. Unlike manuscripts, inscriptions, particularly those on stone, are more resistant to degradation. However, inscriptions on copper plates and other valuable metals, wood, and perishable materials are susceptible to loss, often traded unknowingly by custodians who do not recognize their historical significance, leading to their conversion into everyday items and resulting in the loss of invaluable historical information. Creating authentic replicas of such inscriptions, either from existing primary sources or from secondary sources like published catalogs, is crucial for preserving ancient medical wisdom. Electric engraving pens are essential tools in reproducing copper plate inscriptions accurately. This study is pioneering in its approach to creating replicas of these specific copper plate inscriptions. This article presents a case study of creating replicas of two copper plate inscriptions. The first is the Tummalagudem copper plate inscription (435 AD), written in Telugu–Kannada script, documenting a royal donation of two villages to a monastery for the provision of essential amenities for the sick, including dīpa (lamps), dhūpa (incense), and bhaiṣajya (medicines). The second is the Kaluvacheru copper plate inscription (1423 AD), detailing the Parahitha or Lokopakara family of Ayurvedic physicians, which, although lost in its original form, has been preserved through estampage prints. The successful replication of these inscriptions demonstrates the prudent use of modern technology in preserving authentic historical records for future generations.

Most of the available manuscripts especially Ayurvedic manuscripts are written in the scripts like, grantha sharada, telungu Devanagari, nevari,Tibetan etc in languages like Prākrita, Pāḷi, Saṃskṛta, Apabhraṃśa, Tamiletc .In the critical edition process the first step after collection of manuscript is the transliteration of the manuscripts available in different script into one common script. It indicates the importance of knowledge about different scripts in the critical edition of manuscripts. Objective are to study the different steps in critical edition of manuscripts and to study the importance of paleography in manuscriptology. Transliteration of the manuscript into common script is a crucial step in the edition process of manuscripts. Paleography has pivotal role in critical edition of manuscripts.

Ayurveda being a practical science is codified through centuries in written documents called Manuscripts. A manuscript is any document written by hand or typewritten as opposed to being mechanically printed or reproduced in some automated way. A very few of these manuscripts have been published during the past decades. As such, several treatment methods contained in these texts are being lost by decaying. As part of a humble step towards this, Chikitsasara authored by Gopaladasa, a paper manuscript in the Sanskrit language documented in Devanagari script preserved at Unmesha Research Institute of Indology, Mysore was taken. The objectives of the study are critical edition of the manuscript and its English translation which is not available. It is a unique book belonging to the kayachikitsa parampara. The time period of the text by considering the internal and external evidence, influence of the text on other medieval texts can be placed as the late seventeenth century.The text was translated to Telugu and Marathi languages in 1877 and 1881 respectively, which are not widely available today. Chikitsasara is a treatise arranged in three sections. Some rare diseases like sparsavata, seethavata, takraprameha, and ghrtaprameha have found a place in this text. Prognosis of disease based on astrology is a unique feature of this text. After the critical edition, a maximum number of accepted readings was obtained from the manuscript from Oriental research institute &amp; manuscript library, Kariavattom. The content of the text is also very much similar to the seventeenth century work Yogaratnakara.

&#x0D; &#x0D; &#x0D; &#x0D; translator&#x0D; &#x0D; &#x0D; Ketersediaan Citra Satelit yang berkualitas menjadi salah satu syarat keberhasilan penelitian sumberdaya alam, secara khusus dibidang kehutanan. Google Earth Engine (GEE) adalah salah satu platform berbasis awan (cloud) yang disediakan oleh Google. GEE bekerja berbasis Bahasa program Java Script. Hasil penelitian menunjukan bahwa aplikasi GEE mampu menyediakan citra satelit yang memiliki tutupan awan sangat rendah atau bebas awan (clouds free). Aplikasi GEE merupakan salah satu solusi penelitian sumberdaya alam terutama pada pulau-pulau kecil di Provinsi Maluku.&#x0D; &#x0D; Afrikaans&#x0D; Albanian - shqipe&#x0D; Arabic - ‎‫العربية‬‎&#x0D; Armenian - Հայերէն&#x0D; Azerbaijani - azərbaycanca&#x0D; Basque - euskara&#x0D; Belarusian - беларуская&#x0D; Bengali - বাংলা&#x0D; Bulgarian - български&#x0D; Catalan - català&#x0D; Chinese - 中文（简体中文）&#x0D; Chinese - 中文 (繁體中文)&#x0D; Croatian - hrvatski&#x0D; Czech - čeština&#x0D; Danish - dansk&#x0D; Dutch - Nederlands&#x0D; English&#x0D; Esperanto - esperanto&#x0D; Estonian - eesti&#x0D; Filipino&#x0D; Finnish - suomi&#x0D; French - français&#x0D; Galician - galego&#x0D; Georgian - ქართული&#x0D; German - Deutsch&#x0D; Greek - Ελληνικά&#x0D; Gujarati - ગુજરાતી&#x0D; Haitian Creole - kreyòl ayisyen&#x0D; Hebrew - ‎‫עברית‬‎&#x0D; Hindi - हिन्दी&#x0D; Hungarian - magyar&#x0D; Icelandic - íslenska&#x0D; Indonesian - Bahasa Indonesia&#x0D; Irish - Gaeilge&#x0D; Italian - italiano&#x0D; Japanese - 日本語&#x0D; Kannada - ಕನ್ನಡ&#x0D; Korean - 한국어&#x0D; Latin - Lingua Latina&#x0D; Latvian - latviešu&#x0D; Lithuanian - lietuvių&#x0D; Macedonian - македонски&#x0D; Malay - Bahasa Melayu&#x0D; Maltese - Malti&#x0D; Norwegian - norsk&#x0D; Persian - ‎‫فارسی‬‎&#x0D; Polish - polski&#x0D; Portuguese - português&#x0D; Romanian - română&#x0D; Russian - русский&#x0D; Serbian - Српски&#x0D; Slovak - slovenčina&#x0D; Slovenian - slovenščina&#x0D; Spanish - español&#x0D; Swahili - Kiswahili&#x0D; Swedish - svenska&#x0D; Tamil - தமிழ்&#x0D; Telugu - తెలుగు&#x0D; Thai - ไทย&#x0D; Turkish - Türkçe&#x0D; Ukrainian - українська&#x0D; Urdu - ‎‫اردو‬‎&#x0D; Vietnamese - Tiếng Việt&#x0D; Welsh - Cymraeg&#x0D; Yiddish - יידיש&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Double-click &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Select to translate &#x0D; &#x0D; &#x0D; &#x0D;

LUWES (Live Uninterrupted Water Sensor) adalah alat pencatat elevasi muka air laut yang sensornya dipasang dipermukaan air laut dengan ketinggian tertentu dengan metode gelombang ultrasonic. Alat LUWESdimiliki PUSHIDROSAL (Pusat Hidrografi Dan Oseanografi TNI AngkatanLaut) dipergunakan untuk mencatat data pasang surut dimodifikasi sebagai pencatat gelombang permukaan air laut dengan frequaensi 6 heartz atau enam kali pengukuran dalam 1 detik.Pengamatan pasang surut dibandingkan dengan 3 (tiga) stasiun berada di Teluk Jakartadari pada data gelombangnya.&#x0D; Dari penelitian diperoleh alat LUWES yang dimodifikasi untuk pengukuran gelombang dapat mengukur pasang surut dengan menambahkan bahasa “script pemograman alat” di dalam alat data logger serta menaikkan seri alat sensor Maxbotix MB7363 menjadi Maxbotix serie MB7386. Hasil perhitungan data pengukuran pasang surut dari keempat stasiun relatif sesuai dengan tipe pasang surut harian tunggal. Masing–masing nilai bilangan Formzal keempat stasiun tersebut adalah stasiun Putri Duyung F=4,24, stasiun Marina F=3,90, stasiun BIG Pondok Dayung F=4,24 dan stasiun IOC Kolinlamil F=3,79.

Negara kepulauan Indonesia memiliki wilayah dengan sebagian besar yaitu perairan. Posisinya yang strategis Indonesia menjadi salah satu yang memiliki potensi untuk pelayaran skala kecil antar pulau maupun nasional bahkan internasional. Umumnya dalam mendukung kegiatan pelayaran seperti aktivitas transportasi dalam perairan. Penelitian ini bertujuan untuk mengetahui karakteristik kondisi pasang surut dan membuat peta co-range pasang surut di Teluk Pelabuhan Ratu guna pengembangan pelabuhan yang nantinya akan berfungsi sebagai akses penyaluran barang ke Jakarta. Materi yang digunakan meliputi data primer berupa data analisis konstanta harmonik pasang surut air laut di perairan Teluk Pelabuhan Ratu dari Aviso Altimetry menggunakan data timeseries FES2014 (Finite Element Solution 2014). Sedangkan untuk data sekunder digunakan adalah data elevasi pasang surut air laut pengamatan real time dari iPASOET BIG periode 30 hari di Teluk Pelabuhan Ratu sebagai komparasi terhadap data model yang digunakan Pada penelitian ini mengolah 9 konstanta harmonik pasang surut yaitu K1, O1, P1, K2, M2, S2, N2, M4 dan MS4, dalam pembuatan peta co-range pasang surut. Analisa yang digunakan dalam penelitian ini berupa kuantitatif. Penentuan lokasi penelitian dilakukan dengan menggunakan metode sampling purposive yaitu berdasarkan pada pertimbangan lokasi yang dapat mewakili kondisi daerah penelitian sehingga tujuan penelitian dapat tercapai. Model yang digunakan yaitu software bahasa pemrograman menggunakan script yang telah dibuat untuk membuat peta co-range dan ArcGIS untuk komparasi peta. Hasil penelitian menunjukan perairan Teluk Pelabuhan Ratu yang berbatasan langsung dengan Samudera Hindia memiliki tipe pasang surut campuran condong harian ganda (Mixed Tide Prevailling Semidiurnal). Tipe pasang surut ditunjukkan dengan perhitungan nilai bilangan Formzhal (F) sebesar 0,5 dengan nilai HHWL 346 cm; MSL 300 cm; LLWL 264 cm dan peta co-range memperlihatkan Teluk Pelabuhan Ratu memiliki tinggi amplitudo gelombang pasang surut yang berbeda saat purnama dan perbani. Amplitudo pasang surut di Teluk Pelabuhan Ratu saat pasang tertinggi mencapai 36 cm pada saat purnama dan 0,22 cm pada saat perbani. Saat surut terendah amplitudo pasang surut mencapai -34,75 cm pada saat purnama dan -0,18 cm pada saat perbani.

&lt;em&gt;Along with the progress and development of the business world in Indonesia, especially the city of Bandung, the more advanced the establishment of billboards both isidentil and permanent. The installation of billboards is currently increasing in number and piling up without paying attention to the predetermined procedures for organizing billboards. So it is necessary to carry out supervision or control, this regulation is an obligation of the Bandung City Civil Service Police Unit (Satpol PP) as stated in the Bandung City Regional Regulation Number 02 of 2017 in Article 19 paragraph (1) Challenge the implementation of the regulation of the implementation of advertising. However, in reality, in the implementation of billboards, there are still many people who do not follow the installation procedure and not all violations of billboard organizers can be put in order by satpol PP Bandung City. The purpose of this study is to determine the performance of Satpol PP Bandung City employees in the Regulation of Billboards 2020 and to find out the supporting and inhibiting factors in the control of violations of billboard organizers in Satpol PP Bandung City. The method used in this study is a descriptive method with a qualitative type of research. This is done in the context of collecting primary data by means of observation, interviews and documentation. In addition, data collection was carried out using several book references with research themes to support previous data. The results showed that the performance of Satpol PP Bandung City Employees in the Implementation of Billboard Control has not been optimal, this is evidenced by the many violations of billboard organizers in the city of Bandung. This is due to the lack of personnel and facilities and infrastructure. The way to overcome this is that there must be additional personnel or employees of billboard control and the provision of adequate equipment to support the implementation of advertising control in the field&lt;/em&gt;&lt;textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"&gt;&lt;/textarea&gt;&lt;textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"&gt;&lt;/textarea&gt;&lt;div class="TnITTtw-fp-collapsed-button" style="display: block;"&gt; &lt;/div&gt;&lt;textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"&gt;&lt;/textarea&gt;&lt;div class="TnITTtw-fp-collapsed-button" style="display: block;"&gt; &lt;/div&gt;&lt;textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"&gt;&lt;/textarea&gt;&lt;div id="WidgetFloaterPanels" class="LTRStyle" style="display: none; text-align: left; direction: ltr; visibility: hidden;"&gt;&lt;div id="WidgetFloater" style="display: none;" onmouseover="Microsoft.Translator.OnMouseOverFloater()" onmouseout="Microsoft.Translator.OnMouseOutFloater()"&gt;&lt;div id="WidgetLogoPanel"&gt;&lt;span id="WidgetTranslateWithSpan"&gt;&lt;span&gt;TRANSLATE with &lt;/span&gt;&lt;img id="FloaterLogo" alt="" /&gt;&lt;/span&gt; &lt;span id="WidgetCloseButton" title="Exit Translation" onclick="Microsoft.Translator.FloaterOnClose()"&gt;x&lt;/span&gt;&lt;/div&gt;&lt;div id="LanguageMenuPanel"&gt;&lt;div class="DDStyle_outer"&gt;&lt;input id="LanguageMenu_svid" style="display: none;" onclick="this.select()" type="text" name="LanguageMenu_svid" value="en" /&gt; &lt;input id="LanguageMenu_textid" style="display: none;" onclick="this.select()" type="text" name="LanguageMenu_textid" /&gt; &lt;span id="__LanguageMenu_header" class="DDStyle" onclick="return LanguageMenu &amp;amp;&amp;amp; !LanguageMenu.Show('__LanguageMenu_popup', event);" onkeydown="return LanguageMenu &amp;amp;&amp;amp; !LanguageMenu.Show('__LanguageMenu_popup', event);"&gt;English&lt;/span&gt;&lt;div style="position: relative; text-align: left; left: 0;"&gt;&lt;div style="position: absolute; ;left: 0px;"&gt;&lt;div id="__LanguageMenu_popup" class="DDStyle" style="display: none;"&gt;&lt;table id="LanguageMenu" border="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ar');" tabindex="-1" href="#ar"&gt;Arabic&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('he');" tabindex="-1" href="#he"&gt;Hebrew&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('pl');" tabindex="-1" href="#pl"&gt;Polish&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('bg');" tabindex="-1" href="#bg"&gt;Bulgarian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('hi');" tabindex="-1" href="#hi"&gt;Hindi&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('pt');" tabindex="-1" href="#pt"&gt;Portuguese&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ca');" tabindex="-1" href="#ca"&gt;Catalan&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('mww');" tabindex="-1" href="#mww"&gt;Hmong Daw&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ro');" tabindex="-1" href="#ro"&gt;Romanian&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('zh-CHS');" tabindex="-1" href="#zh-CHS"&gt;Chinese Simplified&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('hu');" tabindex="-1" href="#hu"&gt;Hungarian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ru');" tabindex="-1" href="#ru"&gt;Russian&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('zh-CHT');" tabindex="-1" href="#zh-CHT"&gt;Chinese Traditional&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('id');" tabindex="-1" href="#id"&gt;Indonesian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('sk');" tabindex="-1" href="#sk"&gt;Slovak&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('cs');" tabindex="-1" href="#cs"&gt;Czech&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('it');" tabindex="-1" href="#it"&gt;Italian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('sl');" tabindex="-1" href="#sl"&gt;Slovenian&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('da');" tabindex="-1" href="#da"&gt;Danish&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ja');" tabindex="-1" href="#ja"&gt;Japanese&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('es');" tabindex="-1" href="#es"&gt;Spanish&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('nl');" tabindex="-1" href="#nl"&gt;Dutch&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('tlh');" tabindex="-1" href="#tlh"&gt;Klingon&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('sv');" tabindex="-1" href="#sv"&gt;Swedish&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('en');" tabindex="-1" href="#en"&gt;English&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ko');" tabindex="-1" href="#ko"&gt;Korean&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('th');" tabindex="-1" href="#th"&gt;Thai&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('et');" tabindex="-1" href="#et"&gt;Estonian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('lv');" tabindex="-1" href="#lv"&gt;Latvian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('tr');" tabindex="-1" href="#tr"&gt;Turkish&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('fi');" tabindex="-1" href="#fi"&gt;Finnish&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('lt');" tabindex="-1" href="#lt"&gt;Lithuanian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('uk');" tabindex="-1" href="#uk"&gt;Ukrainian&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('fr');" tabindex="-1" href="#fr"&gt;French&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ms');" tabindex="-1" href="#ms"&gt;Malay&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ur');" tabindex="-1" href="#ur"&gt;Urdu&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('de');" tabindex="-1" href="#de"&gt;German&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('mt');" tabindex="-1" href="#mt"&gt;Maltese&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('vi');" tabindex="-1" href="#vi"&gt;Vietnamese&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('el');" tabindex="-1" href="#el"&gt;Greek&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('no');" tabindex="-1" href="#no"&gt;Norwegian&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('cy');" tabindex="-1" href="#cy"&gt;Welsh&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('ht');" tabindex="-1" href="#ht"&gt;Haitian Creole&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;a onclick="return LanguageMenu.onclick('fa');" tabindex="-1" href="#fa"&gt;Persian&lt;/a&gt;&lt;/td&gt;&lt;td&gt; &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;img style="height: 7px; width: 17px; border-width: 0px; left: 20px;" alt="" /&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;script type="text/javascript"&gt;// &lt;![CDATA[&#x0D; var LanguageMenu; var LanguageMenu_keys=["ar","bg","ca","zh-CHS","zh-CHT","cs","da","nl","en","et","fi","fr","de","el","ht","he","hi","mww","hu","id","it","ja","tlh","ko","lv","lt","ms","mt","no","fa","pl","pt","ro","ru","sk","sl","es","sv","th","tr","uk","ur","vi","cy"]; var LanguageMenu_values=["Arabic","Bulgarian","Catalan","Chinese Simplified","Chinese Traditional","Czech","Danish","Dutch","English","Estonian","Finnish","French","German","Greek","Haitian Creole","Hebrew","Hindi","Hmong Daw","Hungarian","Indonesian","Italian","Japanese","Klingon","Korean","Latvian","Lithuanian","Malay","Maltese","Norwegian","Persian","Polish","Portuguese","Romanian","Russian","Slovak","Slovenian","Spanish","Swedish","Thai","Turkish","Ukrainian","Urdu","Vietnamese","Welsh"]; var LanguageMenu_callback=function(){ }; var LanguageMenu_popupid='__LanguageMenu_popup'; &#x0D; // ]]&gt;&lt;/script&gt;&lt;/div&gt;&lt;div id="CTFLinksPanel"&gt;&lt;span id="ExternalLinksPanel"&gt;&lt;a id="HelpLink" title="Help" href="https://go.microsoft.com/?linkid=9722454" target="_blank"&gt; &lt;img id="HelpImg" alt="" /&gt;&lt;/a&gt; &lt;a id="EmbedLink" title="Get this widget for your own site" href="javascript:Microsoft.Translator.FloaterShowEmbed()"&gt; &lt;img id="EmbedImg" alt="" /&gt;&lt;/a&gt; &lt;a id="ShareLink" title="Share translated page with friends" href="javascript:Microsoft.Translator.FloaterShowSharePanel()"&gt; &lt;img id="ShareImg" alt="" /&gt;&lt;/a&gt; &lt;/span&gt;&lt;/div&gt;&lt;div id="FloaterProgressBar"&gt; &lt;/div&gt;&lt;/div&gt;&lt;div id="WidgetFloaterCollapsed" style="display: none;" onmouseover="Microsoft.Translator.OnMouseOverFloater()"&gt;&lt;span&gt;TRANSLATE with &lt;/span&gt;&lt;img id="CollapsedLogoImg" alt="" /&gt;&lt;/div&gt;&lt;div id="FloaterSharePanel" style="display: none;"&gt;&lt;div id="ShareTextDiv"&gt;&lt;span id="ShareTextSpan"&gt; COPY THE URL BELOW &lt;/span&gt;&lt;/div&gt;&lt;div id="ShareTextboxDiv"&gt;&lt;input id="ShareTextbox" onclick="this.select()" type="text" name="ShareTextbox" readonly="readonly" /&gt; &lt;!--a id="TwitterLink" title="Share on Twitter"&gt; &lt;img id="TwitterImg" /&gt;&lt;/a&gt; &lt;a-- id="FacebookLink" title="Share on Facebook"&gt; &lt;img id="FacebookImg" /&gt;&lt;/a--&gt; &lt;a id="EmailLink" title="Email this translation"&gt; &lt;img id="EmailImg" alt="" /&gt;&lt;/a&gt;&lt;/div&gt;&lt;div id="ShareFooter"&gt;&lt;span id="ShareHelpSpan"&gt;&lt;a id="ShareHelpLink"&gt; &lt;img id="ShareHelpImg" alt="" /&gt;&lt;/a&gt;&lt;/span&gt; &lt;span id="ShareBackSpan"&gt;&lt;a id="ShareBack" title="Back To Translation" href="javascript:Microsoft.Translator.FloaterOnShareBackClick()"&gt; Back&lt;/a&gt;&lt;/span&gt;&lt;/div&gt;&lt;input id="EmailSubject" type="hidden" name="EmailSubject" value="Check out this page in {0} translated from {1}" /&gt; &lt;input id="EmailBody" type="hidden" name="EmailBody" value="Translated: {0}%0d%0aOriginal: {1}%0d%0a%0d%0aAutomatic translation powered by Microsoft® Translator%0d%0ahttp://www.bing.com/translator?ref=MSTWidget" /&gt; &lt;input id="ShareHelpText" type="hidden" value="This link allows visitors to launch this page and automatically translate it to {0}." /&gt;&lt;/div&gt;&lt;div id="FloaterEmbed" style="display: none;"&gt;&lt;div id="EmbedTextDiv"&gt;&lt;span id="EmbedTextSpan"&gt;EMBED THE SNIPPET BELOW IN YOUR SITE&lt;/span&gt; &lt;a id="EmbedHelpLink" title="Copy this code and place it into your HTML."&gt; &lt;img id="EmbedHelpImg" alt="" /&gt;&lt;/a&gt;&lt;/div&gt;&lt;div id="EmbedTextboxDiv"&gt;&lt;input id="EmbedSnippetTextBox" onclick="this.select()" type="text" name="EmbedSnippetTextBox" value="&amp;lt;div id='MicrosoftTranslatorWidget' class='Dark' style='color:white;background-color:#555555'&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;script type='text/javascript'&amp;gt;setTimeout(function(){var s=document.createElement('script');s.type='text/javascript';s.charset='UTF-8';s.src=((location &amp;amp;&amp;amp; location.href &amp;amp;&amp;amp; location.href.indexOf('https') == 0)?'https://ssl.microsofttranslator.com':'http://www.microsofttranslator.com')+'/ajax/v3/WidgetV3.ashx?siteData=ueOIGRSKkd965FeEGM5JtQ**&amp;amp;ctf=true&amp;amp;ui=true&amp;amp;settings=manual&amp;amp;from=en';var p=document.getElementsByTagName('head')[0]||document.documentElement;p.insertBefore(s,p.firstChild); },0);&amp;lt;/script&amp;gt;" readonly="readonly" /&gt;&lt;/div&gt;&lt;div id="EmbedNoticeDiv"&gt;&lt;span id="EmbedNoticeSpan"&gt;Enable collaborative features and customize widget: &lt;a href="http://www.bing.com/widget/translator" target="_blank"&gt;Bing Webmaster Portal&lt;/a&gt;&lt;/span&gt;&lt;/div&gt;&lt;div id="EmbedFooterDiv"&gt;&lt;span id="EmbedBackSpan"&gt;&lt;a title="Back To Translation" href="javascript:Microsoft.Translator.FloaterOnEmbedBackClick()"&gt;Back&lt;/a&gt;&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;&lt;script type="text/javascript"&gt;// &lt;![CDATA[&#x0D; var intervalId = setInterval(function () { if (MtPopUpList) { LanguageMenu = new MtPopUpList(); var langMenu = document.getElementById(LanguageMenu_popupid); var origLangDiv = document.createElement("div"); origLangDiv.id = "OriginalLanguageDiv"; origLangDiv.innerHTML = "&lt;span id='OriginalTextSpan'&gt;ORIGINAL: &lt;/span&gt;&lt;span id='OriginalLanguageSpan'&gt;&lt;/span&gt;"; langMenu.appendChild(origLangDiv); LanguageMenu.Init('LanguageMenu', LanguageMenu_keys, LanguageMenu_values, LanguageMenu_callback, LanguageMenu_popupid); window["LanguageMenu"] = LanguageMenu; clearInterval(intervalId); } }, 1); &#x0D; // ]]&gt;&lt;/script&gt;&lt;/div&gt;&lt;div class="TnITTtw-fp-collapsed-button" style="display: block;"&gt; &lt;/div&gt;&lt;div class="TnITTtw-mate-fp-bar" style="z-index: 2; width: 0px; height: 0px; opacity: 0; display: none;"&gt;&lt;div class="TnITTtw-hide-fp-bar" style="display: none;"&gt; &lt;/div&gt;&lt;div class="TnITTtw-current-page-lang" style="display: none;"&gt;This page is in English&lt;/div&gt;&lt;div class="TnITTtw-cta-button-layout" style="display: none;"&gt;&lt;div class="TnITTtw-spinner"&gt; &lt;/div&gt;&lt;div class="TnITTtw-mw-button TnITTtw-fp-translate TnITTtw-high-cta" style="display: none;"&gt;Translate to Indonesian&lt;/div&gt;&lt;/div&gt;&lt;div class="TnITTtw-change-language TnITTtw-select" style="display: none;" data-for-serial="3"&gt; &lt;/div&gt;&lt;div class="TnITTtw-stop-fp"&gt; &lt;/div&gt;&lt;div class="TnITTtw-toggle-iphone-settings" style="display: none;"&gt; &lt;/div&gt;&lt;div class="TnITTtw-ui_selector" style="display: none;"&gt;&lt;div class="TnITTtw-options-arrow"&gt; &lt;/div&gt;&lt;div class="TnITTtw-options TnITTtw-opt-3 TnITTtw-standalone" style="display: none; z-index: 998;" data-serial="3"&gt;&lt;div class="TnITTtw-dd-search"&gt;&lt;input class="TnITTtw-dd-input" type="text" data-dir="to" data-width="NaN" /&gt;&lt;/div&gt;&lt;div id="selVisibleScroll-3"&gt;&lt;div id="selEntireScroll-3"&gt;&lt;div class="TnITTtw-inner-options-layout"&gt;&lt;ul class="TnITTtw-list"&gt;&lt;li class="lang-af TnITTtw-option"&gt;&lt;span id="lang-af" class="lang-af"&gt;Afrikaans&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-sq TnITTtw-option"&gt;&lt;span id="lang-sq" class="lang-sq"&gt;Albanian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-am TnITTtw-option"&gt;&lt;span id="lang-am" class="lang-am"&gt;Amharic&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ar TnITTtw-option"&gt;&lt;span id="lang-ar" class="lang-ar"&gt;Arabic&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-hy TnITTtw-option"&gt;&lt;span id="lang-hy" class="lang-hy"&gt;Armenian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-az TnITTtw-option"&gt;&lt;span id="lang-az" class="lang-az"&gt;Azerbaijani&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-bn TnITTtw-option"&gt;&lt;span id="lang-bn" class="lang-bn"&gt;Bengali&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-bg TnITTtw-option"&gt;&lt;span id="lang-bg" class="lang-bg"&gt;Bulgarian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ca TnITTtw-option"&gt;&lt;span id="lang-ca" class="lang-ca"&gt;Catalan&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-hr TnITTtw-option"&gt;&lt;span id="lang-hr" class="lang-hr"&gt;Croatian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-cs TnITTtw-option"&gt;&lt;span id="lang-cs" class="lang-cs"&gt;Czech&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-da TnITTtw-option"&gt;&lt;span id="lang-da" class="lang-da"&gt;Danish&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-nl TnITTtw-option"&gt;&lt;span id="lang-nl" class="lang-nl"&gt;Dutch&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-en TnITTtw-option"&gt;&lt;span id="lang-en" class="lang-en"&gt;English&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-et TnITTtw-option"&gt;&lt;span id="lang-et" class="lang-et"&gt;Estonian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-fi TnITTtw-option"&gt;&lt;span id="lang-fi" class="lang-fi"&gt;Finnish&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-fr TnITTtw-option"&gt;&lt;span id="lang-fr" class="lang-fr"&gt;French&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-de TnITTtw-option"&gt;&lt;span id="lang-de" class="lang-de"&gt;German&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-el TnITTtw-option"&gt;&lt;span id="lang-el" class="lang-el"&gt;Greek&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-gu TnITTtw-option"&gt;&lt;span id="lang-gu" class="lang-gu"&gt;Gujarati&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ht TnITTtw-option"&gt;&lt;span id="lang-ht" class="lang-ht"&gt;Haitian Creole&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-iw TnITTtw-option"&gt;&lt;span id="lang-iw" class="lang-iw"&gt;Hebrew&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-hi TnITTtw-option"&gt;&lt;span id="lang-hi" class="lang-hi"&gt;Hindi&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-hu TnITTtw-option"&gt;&lt;span id="lang-hu" class="lang-hu"&gt;Hungarian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-is TnITTtw-option"&gt;&lt;span id="lang-is" class="lang-is"&gt;Icelandic&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-id TnITTtw-option_selected"&gt;&lt;span id="lang-id" class="lang-id"&gt;Indonesian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-it TnITTtw-option"&gt;&lt;span id="lang-it" class="lang-it"&gt;Italian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ja TnITTtw-option"&gt;&lt;span id="lang-ja" class="lang-ja"&gt;Japanese&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-kn TnITTtw-option"&gt;&lt;span id="lang-kn" class="lang-kn"&gt;Kannada&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-kk TnITTtw-option"&gt;&lt;span id="lang-kk" class="lang-kk"&gt;Kazakh&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-km TnITTtw-option"&gt;&lt;span id="lang-km" class="lang-km"&gt;Khmer&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ko TnITTtw-option"&gt;&lt;span id="lang-ko" class="lang-ko"&gt;Korean&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ku TnITTtw-option"&gt;&lt;span id="lang-ku" class="lang-ku"&gt;Kurdish (Kurmanji)&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-lo TnITTtw-option"&gt;&lt;span id="lang-lo" class="lang-lo"&gt;Lao&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-lv TnITTtw-option"&gt;&lt;span id="lang-lv" class="lang-lv"&gt;Latvian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-lt TnITTtw-option"&gt;&lt;span id="lang-lt" class="lang-lt"&gt;Lithuanian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-mg TnITTtw-option"&gt;&lt;span id="lang-mg" class="lang-mg"&gt;Malagasy&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ms TnITTtw-option"&gt;&lt;span id="lang-ms" class="lang-ms"&gt;Malay&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ml TnITTtw-option"&gt;&lt;span id="lang-ml" class="lang-ml"&gt;Malayalam&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-mt TnITTtw-option"&gt;&lt;span id="lang-mt" class="lang-mt"&gt;Maltese&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-mi TnITTtw-option"&gt;&lt;span id="lang-mi" class="lang-mi"&gt;Maori&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-mr TnITTtw-option"&gt;&lt;span id="lang-mr" class="lang-mr"&gt;Marathi&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-my TnITTtw-option"&gt;&lt;span id="lang-my" class="lang-my"&gt;Myanmar (Burmese)&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ne TnITTtw-option"&gt;&lt;span id="lang-ne" class="lang-ne"&gt;Nepali&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-no TnITTtw-option"&gt;&lt;span id="lang-no" class="lang-no"&gt;Norwegian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ps TnITTtw-option"&gt;&lt;span id="lang-ps" class="lang-ps"&gt;Pashto&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-fa TnITTtw-option"&gt;&lt;span id="lang-fa" class="lang-fa"&gt;Persian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-pl TnITTtw-option"&gt;&lt;span id="lang-pl" class="lang-pl"&gt;Polish&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-pt TnITTtw-option"&gt;&lt;span id="lang-pt" class="lang-pt"&gt;Portuguese&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-pa TnITTtw-option"&gt;&lt;span id="lang-pa" class="lang-pa"&gt;Punjabi&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ro TnITTtw-option"&gt;&lt;span id="lang-ro" class="lang-ro"&gt;Romanian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ru TnITTtw-option"&gt;&lt;span id="lang-ru" class="lang-ru"&gt;Russian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-sm TnITTtw-option"&gt;&lt;span id="lang-sm" class="lang-sm"&gt;Samoan&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-zh-CN TnITTtw-option"&gt;&lt;span id="lang-zh-CN" class="lang-zh-CN"&gt;Simplified Chinese&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-sk TnITTtw-option"&gt;&lt;span id="lang-sk" class="lang-sk"&gt;Slovak&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-sl TnITTtw-option"&gt;&lt;span id="lang-sl" class="lang-sl"&gt;Slovenian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-es TnITTtw-option"&gt;&lt;span id="lang-es" class="lang-es"&gt;Spanish&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-sv TnITTtw-option"&gt;&lt;span id="lang-sv" class="lang-sv"&gt;Swedish&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ta TnITTtw-option"&gt;&lt;span id="lang-ta" class="lang-ta"&gt;Tamil&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-te TnITTtw-option"&gt;&lt;span id="lang-te" class="lang-te"&gt;Telugu&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-th TnITTtw-option"&gt;&lt;span id="lang-th" class="lang-th"&gt;Thai&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-zh-TW TnITTtw-option"&gt;&lt;span id="lang-zh-TW" class="lang-zh-TW"&gt;Traditional Chinese&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-tr TnITTtw-option"&gt;&lt;span id="lang-tr" class="lang-tr"&gt;Turkish&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-uk TnITTtw-option"&gt;&lt;span id="lang-uk" class="lang-uk"&gt;Ukrainian&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-ur TnITTtw-option"&gt;&lt;span id="lang-ur" class="lang-ur"&gt;Urdu&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-vi TnITTtw-option"&gt;&lt;span id="lang-vi" class="lang-vi"&gt;Vietnamese&lt;/span&gt;&lt;/li&gt;&lt;li class="lang-cy TnITTtw-option"&gt;&lt;span id="lang-cy" class="lang-cy"&gt;Welsh&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;div id="sel-scrollbar-3"&gt; &lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="TnITTtw-fp-options" style="display: none;"&gt;&lt;input id="TnITTtw-always-translate" style="display: none;" type="checkbox" readonly="readonly" /&gt;&lt;label class="TnITTtw-always-translate-label TnITTtw-not-pro" style="display: none;" for="TnITTtw-always-translate"&gt;&lt;span class="TnITTtw-always-translate-inner-label" style="display: none;"&gt;Always translate English to Indonesian&lt;/span&gt;&lt;span class="TnITTtw-pro-label" style="display: none;"&gt;PRO&lt;/span&gt;&lt;/label&gt; &lt;br style="display: none;" /&gt; &lt;input id="TnITTtw-never-translate-lang" style="display: none;" type="checkbox" /&gt;&lt;label class="TnITTtw-never-translate-lang-label" style="display: none;" for="TnITTtw-never-translate-lang"&gt;Never translate English&lt;/label&gt; &lt;br style="display: none;" /&gt; &lt;input id="TnITTtw-never-translate-site" style="display: none;" type="checkbox" /&gt;&lt;label class="TnITTtw-never-translate-site-label" style="display: none;" for="TnITTtw-never-translate-site"&gt;Never translate jurnal.unikal.ac.id&lt;/label&gt;&lt;/div&gt;&lt;/div&gt;

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The middle class is placed between labour and capital. It neither directly awns the means of production that pumps out the surplus generated by wage labour power, nor does it, by its own labour, produce the surplus which has use and exchange value. Broadly speaking, this class consists of the petty bourgeoisie and the white-collar workers. The former are either self-employed or involved in the distribution of commodities and the latter are non-manual office workers, supervisors and professionals. Thus, in terms of occupation, shopkeepers, salesmen, brokers, government and non-government office-workers, writers, teachers, and self-employed professionals, such as engineers, pleaders, doctors, etc., constitute the middle class. Most of these occupations require at least some degree of formal education. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; This middle class is primarily a product of capitalist development and the expansion of the functions of the state in the nineteenth and twentieth centuries. Though the petty bourgeoisie and managers did exist in precapitalist society, they constituted a tiny class. Industrial development and expansion of markets require not only a larger managerial class than earlier, but also impel the state to shoulder the responsibilities of monitoring market competition and resolving the contradictions of capitalist development. This includes formation and implementation of welfare programmes to minimise tension in society. For carrying out these functions, the state also requires a managerial class. Formal education contributes to the expansion of this class. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; It is difficult to estimate the size of this class in contemporary India. It is certainly very large. According to the calculations made by Ranjit Sahu (1986), the number of white-collar employees is larger than that of industrial workers.&rsquo; A large majority of the members of the middle class belong to the upper and middle castes. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; While scanning literature on the subject, one is disappointed at the absence of studies on middle-class movements per se, whereas one finds studies on peasant, working-class or tribal movements. This is not because the middle-class movements are few in number, nor because scholars have an aversion towards the middle class. They do take cognisance of the role of the middle class in various movements. But these movements are primarily analysed in terms of the issues that they raise, such as social reform movements, the nationalist movement, human rights movements, ecology movements, and so on. Or, these movements are called &lsquo;mass movements&rsquo;, as the issues are not class specific, nor affecting mainly the middle class. The issues are posed as societal problems. The leaders of such movements, who belong to the middle class, mobilise other classes for support. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; In this section, I shall deal with the studies on those movements in which I believe the middle class played a prominent role as initiators, and those where a majority of the participants belong to the middle class. Though students also belong to this class, we have dealt with their movements separately. British rule established and introduced a capitalist economy, a new administrative system and English education in the early nineteenth century. Consequently, a tiny educated class emerged in urban areas (Desai 1957; Mishra 1978). The members of this class were upper-caste Hindus. Muslims were, for a variety of reasons late in availing of an English education (Seal 1968). A few individuals in different parts of the country not only raised questions but also revolted against certain customs and traditions of the Hindu social system. These individuals, known as social and religious reformers, were all those who were advocates of alterations in social customs which would involve a break with traditionally accepted patterns; they were those who, convinced themselves that altered ways of thinking and behaving were positive values, sought to convince others to modify or entirely transform their ways of life&rsquo; (Heimsath 1964: 4). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The reformers took up several issues. They included elimination of or change in certain caste regulations and rituals: the sari system, widow remarriage, child marriage, status of women, girls&rsquo; education, prohibition, etc. Though a few talked against the caste hierarchy and untouchability, most of the reformers (except a few who led the anti-Brahmin movement), did not challenge the social structure. They adopted a gradualist approach. Heimsath argues, In India, social reform did not ordinarily mean a reorganisation of the structure of society at large, as it did in the West, for the benefit of underprivileged social economic classes. Instead it meant the infusion into the existing social structure of new ways of life and thought: the society would be preserved, while its members would be transformed (1964: 5). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The reformers either revolted individually or formed associations. These associations were of three types: general (or voluntary) associations; caste reform associations and religious reform bodies (generally called samaj) (Heimsath 1964). The Indian National Social Conference was formed in 1887. Social reform associations came into existence at provincial and local levels. Some of them were formed around one issue, such as widow remarriage or marriageable age, child marriage, whereas others took up general issues related to social reform, protesting against &lsquo;conservatism&rsquo;, including protests against religious heads, superstitions, caste restrictions for crossing the sea, etc. They were loose organisations whose activities were largely confined to programmes, conferences and passing resolutions. A few of them turned into charity organisations and undertook welfare programmes&mdash;particularly in education. Some reformers confined their activities to their caste. They formed caste associations and persuaded caste fellows to join for the reformation of certain unacceptable practices which they felt were either inhuman or did not fit in with the changing times. The most prominent associations were related to religious reforms. Raja Rammohan Roy, who protested against the sati system, formed the Brahmo Samaj which remained the centre for social reform activities in Bengal (Kopf 1979). The Prarthana Samaj came into existence in Bombay under the leadership of Mahadev Govind Ranade (Tucker 1977). The Arya Samaj, formed by Dayanand Saraswati, was the predominant influence in Punjab and north India (Jones 1968; Jordens 1977; Vable 1983). On the whole, social reform movements were weak in south India, despite the presence of a large number of western-educated persons. Heimsath observes that &lsquo;the region produced no reformer of national standing and only a few with lasting local influence&rsquo; (1964: 253). It should be noted that the backward-caste movement as an anti-Brahmin movement was prominent in the Madras Presidency; which we have dealt with later. The main thrust of the socio-religious reform movements was to revive or rejuvenate Hindu religion and society. This was, according to many scholars, to counter the impact of western culture and the efforts of proselytisation by Christian missionaries (Heimsath 1964; Jones 1968; Bhatt 1973; Sun 1977; Jordens 1977). K. P. Gupta (1974), in his study on the Ramakrishna Mission, refutes this position. He argues that the &lsquo;innovative potentiality&rsquo; of Hinduism was more responsible for its rejuvenation rather than the threat or impact of other religions or cultures. According to A.R. Desai, the traditional social structure and religion were not able to cope with the new economic structure which was based on individualism. The reformers were therefore striving &lsquo;to extend the principle of individual liberty to the sphere of religion (1957: 258). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The social reform movements among Hindus and Muslims contributed to the development of nationalism on regional and religious lines. There were several kinds of nationalisms competing with each other. Anil Seal argues, There were keen internal rivalries, but these were between caste and caste, community, not between class and class. Moreover, those groups which felt a similarity of interest were themselves more the product of bureaucratic initiative than of economic change. Since these groups can be largely identified with the men educated in western styles, and since it was these men whose hopes and fears went into the building of the new associations that emerged as the Indian National Congress, a conceptual system based on elites, rather than on classes, would seem more promising (1968: 341). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; These elite belong to the middle class. Granting that the initiative came from the bureaucracy, it was intended to bring about economic change in society in general and the middle class in particular. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The middle class participated at various stages of India&rsquo;s freedom movement. The major events of their collective action were the partition of Bengal in 1906, the non-cooperation campaign in the early 1920s, the anti-Simon agitation in the mid-1920s, Civil Disobedience movements in the early 1930s, and the Quit India movement in 1942. Besides this, there were a number of local-level campaigns&mdash;organised and spontaneous&mdash;against the British Raj. Though there are a large number of studies on the freedom movement, most of them are mainly focused on the leadership and their decisions. In his study on popular movements between 1945 and 1947, Sumit Sarkar argues that, &lsquo;in this as well as in other periods of modern Indian history, the decisions and actions of leaders, British or Indian, cannot really be understood without the counterpoint provided by pressures from below¹ (1982: 677). A few studies on the Bang-bhang movement, the Civil Disobedience movement and the Quit India movement, point out that there were close links between local politics and national agitations (Stoddart 1975). Use of religious and communal idioms and violence are examined by some other scholars (Irschick 1976; Hennigham 1979). The communal dimension of the participants has been highlighted by some studies. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Social reform among the Muslims began with the Aligarh movement led by Syed Ahmad Khan. The main thrust of the movement was to persuade the Muslim landed gentry to take an English education. Without English education, it was feared that the Muslims would be unable to compete with the Hindus and would remain backward. M.S.Jain (1965) argues that che spirit behind the Aligarh movement was to reassert Muslim superiority over the Hindus, which the former had lost during the early phase of the British rule. The movement generated the urge for a &lsquo;separate and independent status&rsquo; for the Muslims. The Ullama of Uttar Pradesh opposed the Aligarh movement and the subsequent demand for a separate state for Muslims (Faruqi 1963). The Khilafat movement (1919-24) led by the Muslim intelligentsia and the Ullama, mobilised a cross section of the Muslims. Their claim was that the Sultan of Turkey was the custodian and defender, the protector of the holy places known as Jazirat al-Arab. The movement was supported by all the Muslim groups and by the Indian National Congress (Dixit 1969; Hasan 1981). Religious symbols, like the mosque, the haji, sufi shrines, provided a sense of belonging to the common fraternity of Islam in India (Hasan 1981). Generally, the &lsquo;divide and rule&rsquo; policy of the British rulers, Muslim orthodoxy, and the educational and economic backwardness of the Muslims, are considered to be responsible for the growth of communal Muslim politics (Desai 1957; Smith 1963). Prabha Dixit (1974) argues that a search for power was responsible for communal politics (see also Broomfield 1968). It is the argument of many scholars that the nationalist movement failed to develop secular symbols. The nationalist movement was dominated by the Hindus who used Hindu religious symbols and idioms for the freedom movement (Smith 1963; Ahmad 1969). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Seal (1968) and Brass (1970) refute the general argument regarding the backwardness of the Muslims. They point out that they were far from being backward in the Muslim-minority provinces. Gopal Krishna argues that &lsquo;it would seem that sociologically the communal movement was a movement of the privileged rather than of the deprived sections of the Muslim population (1981: 55). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A number of Hindu and Muslim communal organisations have come into existence in post-independence India. Through various programmes, they strengthen communal identities and stereotypes for each other. Sensitive issues are raised and articulated. These organisations play an important role in rousing communal sentiments. The number of communal riots has increased since the 1950s. Apart from a large number of journalistic writings and government-appointed inquiry commissions&rsquo; reports, a few case studies by social scientists and activists are now available (Shah 1970; Engineer and Shakir 1985; Van der Veer 1987; Brass 1996, 1998; Horowitz 2001). They highlight not only communal antagonisms, but also economic factors in mobilising members of both communities against each other. Some studies focus on the manipulation of the elite in rousing sentiments leading to riots (Patel 1985). By now we have a good deal of documents on communal riots which include government reports and also reports by independent citizens as well as human rights groups and non-government organisations (NGOs). Systematic comparative studies on communally based mobilisation into riots need to be undertaken to understand the complexities of the phenomenon. There is a good deal of literature on secularisation, nationalism and communal politics. This requires a full-fledged review. We have excluded it from the scope of the present work. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The upper-caste Hindu middle class launched struggles in Bihar and Gujarat against reservation for the Scheduled Castes, Scheduled Tribes and other backward classes. Upper-caste government servants also launched agitations against the roster system which provided certain benefits to Scheduled Caste/Scheduled Tribe employees. These agitations were primarily the result of the conflict of economic interests between upper and deprived caste groups; the middle-class leaders of these agitations raised the issue of &lsquo;merit&rsquo;, &lsquo;secularism&rsquo; and &lsquo;efficiency&rsquo;. While analysing the 1981 anti-reservation agitation in Gujarat, I.P. Desai argues that the economic structure was not able to provide employment opportunities for the lower strata of the higher castes. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The higher castes therefore wish to prevent the mobility of lower castes and contain the discontent among the lower strata of higher castes by appealing to the concealed caste sentiment among them and speaking publicly against casteism, communalism, reservation and all that is particularistic, narrow and parochial. Thus although &ldquo;merit&rdquo; appears to be a progressive slogan, it is in fact a weapon for defending the moribund Hindu hierarchy and maintain [the] social economic status quo (1985: 135). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; By now, there are a few articles on the Gujarat anti-reservation agitation (Yagnik 1981; Bose 1985; Shah 1987) providing information about the socio-economic and political dimensions of the conflict between the upper castes and the deprived groups. Shah (1987) argues that these two agitations were essentially struggles within the middle class. They were fights between the upper- and middle-caste members on the one hand, and the new entrants from the low castes on the other. Some sections of the middle class&mdash;white-collar government employees, school and university teachers, etc.&mdash;launched movements on economic issues affecting them, such as, revision of pay scales, bonus, job security. Though there is no systematic study on the struggles, a few descriptive accounts and analyses of the demands are available. A few of the recent movements led by the middle class began with economic issues, like price rise, scarcity of essential commodities and unemployment. But in the course of the development of these movements, these issues were sidetracked and the movements raised populist issues, which appeal to various classes. They raise moral and cultural issues. They sometimes provide an ideological basis for &lsquo;democratic capitalism&rsquo; and sometimes also for &lsquo;non-capitalist development strategy&rsquo; (Khoros 1980). Take the case of the 1974 Gujarat movement, popularly known as the Nav Nirman (reconstruction) movement, and the Bihar movement known as the movement for total revolution. Though both these movements began with economic issues, they also raised the issues of corruption, democratic rights and social reform- These issues were not spelled out, nor were they linked with the economic and political structure of the society. They succeeded in ousting the chief minister in Gujarat and the Congress party in Bihar (Desai 1974; Wood 1975; Jones and Jones 1976; Barik 1977; Shah 1977). Ghanshyam Shah (1977) observes that they wanted more economic benefits by bringing about certain changes in the system. &lsquo;They do not believe in changing the basic aspects of the system. They have a stake in the system. To them Revolution is a slogan.&rsquo; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; At the end of the nineteenth century, the educated Hindu middle class of Bihar and Uttar Pradesh launched a series of agitations for the removal of Urdu and for its replacement by Hindi in the Devnagri script. Muslim intellectuals also launched a counter-agitation in defence of Urdu (Das Gupta 1970; Brass 1977). The middle class of south India launched struggles during the 1950s and 1960s against the &lsquo;imposition&rsquo; of Hindi and for the retention of English. For them it was a struggle against Hindu imperialism (Hardgrave 1965; Forrester 1966; and Rao 1979). The middle class of linguistic groups such as Marathi, Gujarati, Tclugu and Punjabi, demanded the formation of linguistic states in the 1950s. They launched agitations for these demands (Phadke l979; Nijhawan 1982). For maintenance of their cultural identity, the middle class among the Tamilian, the Punjabi, the Naga, the Mizo populations, the tribals of Chhota Nagpur area, spearheaded agitations for the formation of separate states within or outside the Indian Union. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; We have already discussed the demands of the Naga, the Mizo, and the tribals of Chhota Nagpur and other tribals for separate states or districts (see Chapter 3). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Dravidian movement in Tamil Nadu was a backward caste or non-Biahmin movement with which we have dealt earlier. It was also directed against north Indians, and demanded a separate state named &lsquo;Dravidisthan&rsquo;, i.e., homeland of the Dravidians outside the Indian Union (Hardgrave 1964, 1965, 1979; Irschick 1976; Ram 1979). Periyar E.V. Ramaswamy, a leader of the Dravidian movement said, &lsquo;Tamil Nadu was all along a nation and still it is a nation and that is known as Dravidian. Civilisation, customs and manners of Tamils are different from that of Bengalees and Bombayans.... Hindi language and literature are opposed to the interests of Tamilians in general and to all other non-Brahmins elsewhere, in particular.&rsquo; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Sikh community of Punjab also demanded a separate state called Khalistan. The Shri Anandpur Sahib Resolution demanded that one of the aims of the Akali Dal be &lsquo;maintaining the feeling of a separate independent entity of the Sikh Panth and creation of an environment in which the &ldquo;National Expression&rdquo; of the Sikhs can be full and satisfactory&rsquo; (Dhillon 1974; Puri 1981, 1983; Kumar et al. 1984; Kumar 1984). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Assam agitation, which began in the late 1970s with the formation of the All Assam Students&rsquo; Union, also raised issues regarding the identity of the Assamese and the development of Assam. In a sense, it was a &lsquo;nationality&rsquo; movement (Mira 1982; Gohain 1985, Basu 1992). Regional or linguistic identities have been sharpened in India since independence and they have become a potential force in mobilising the middle class which faces competition from other classes in the economic field. Robert Hardgrave asserts: Regionalism is rooted in India&rsquo;s cultural and linguistic diversity. Projected in geographical terms, it is at the state level both an ethnic and economic phenomenon. It is an expression of heightened political consciousness, expanding participation and increasing competition for scarce resources.... Economic grievances expressed in charges of unfairness, discrimination or Centre neglect may be fused with cultural anxiety over language status and ethnic balance. It is this fusion that gives regionalism its potency. Language and culture, like religion, are at the core of an individual&rsquo;s identity and when politicized take a potentially virulent form (1983: 1171). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Most social scientists have seen these movements as &lsquo;dysfunctional&rsquo; or a threat to national &lsquo;unity&rsquo; and &lsquo;integration&rsquo;. They believe that the Indian nation state should maintain its boundaries and hold its territory together. Therefore, they are unable to view these struggles as movements for &lsquo;self-determination&rsquo; (Mohanty 1982). <strong>Nativism:</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; There is a very thin line between &lsquo;nativism&rsquo; and &lsquo;nationalism&rsquo;: Katzenstein argues, &lsquo;Nativism ... is distinct from movements of ethnic, linguistic or regional subnationalism, and is specifically anti-migrant. Sub-national movements, such as in India the Akali Dal or Dravida Munnetia Kazhagam, may contain nativist elements, similarly, the mobilisation of anti-migrant sentiment may rely on ethnic, linguistic or regional loyalties&rsquo; (1976: 44). According to Myron Weiner, nativism is one form of ethnic politics. Nativism is that form of ethnic identity that seeks to exclude those who are not members of the local or indigenous ethnic groups from residing and/or working in a territory because they are not native to the country or region: nativism is anti-migrant. To the extent that the D.M.K., the Akali Dal, the Andhra Mahasabha, and the Samyukta Maharashtra Samiti were not anti-migrant, they should not be classified as nativist (1978: 296). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Nativist movements are not of recent origin, there were a few such movements before independence. For instance, the movement against the non-mulki developed in Hyderabad soon after World War I, when the local educated population expressed its opposition to the government policy of recruiting Muslims from northern India into the state administrative services (Weiner 1978; Reddy and Sharma 1979). Similarly, the anti-Bengali movement in Assam protested against the domination of their educational and administrative services by Bengali Hindus (Weiner 1978; Das 1982). In post-independence India, the widely known movements are: the Telengana Nativist movement, the Shiv Sena movement in Maharashtra and the Assam movement- The Telengana nativist agitation began in 1969. Initially, the agitation was aimed at the continuance of Telengana &lsquo;safeguards&rsquo; and mulki rules formulated at the time of the formation of Andhra Pradesh in 1956. At a later stage, it demanded separation of the region from the rest of Andhra Pradesh (Reddy and Sharma 1979). The Shiv Sena (i.e., the army of Shivaji) movement was initiated in 1966 in Bombay. It demanded that as Bombay was the capital of Maharashtra, Maharashtrians should be given the opportunity to make the most of what their capital city had to offer. They asked that 80 per cent of all jobs and economic opportunities in Bombay should be reserved for Maharashtrian; (Joshi 1970; Katzenstein 1976; Gupta 1982). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Assam movement began in 1978. Its main demands included the detection, deportation and deletion of foreigners&rsquo; names from electoral rolls. The movement aimed at the ouster of the Bengali middle class which enjoyed a major share in government jobs (Weiner 1978; Das 1982). Similarly, during the late 1960s the Kannada Chaluvaligar (i.e., agitation) demanded restrictions against Tamil, Malayali, and Telugu migrants to Bangalore and preference for the local Kannada-speaking population (Weiner 1978). The underlying reason for the issue of nativism is competition for government jobs between the natives and the migrants. The cities and regions where nativist movements took place have the following characteristics: The locale contains a substantial number of middle-class migrants belonging to culturally distinguishable ethnic groups originating from another section of the country; There is a native middle class, expanding under the impetus of a growth in secondary and higher education; There is a highly competitive labour market in which the native middle class seeks employment in private and public sector firms and in government, where middle-class positions are already held by migrants or their descendents; There are limited opportunities for the native middle class to find employment outside their own locale (Weiner 1978: 293). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A small section of the urban middle-class intelligentsia&mdash;university and college teachers, researchers and lawyers&mdash;has formed organisations at state and national levels for the protection of &lsquo;civil&rsquo; and &ldquo;democratic¹ rights. They raise issues related to violation of &lsquo;civil¹ and democratic rights of various strata of society, including the oppressed classes (Desai 1986). The existing constitutional channels, such as the judiciary, the state assemblies and Parliament are used for challenging the government&rsquo;s decisions and the power of vested interests. The media is used to highlight issues and create public opinion. Fact-finding committees are appointed. The intelligentsia has also raised ecological issues. They organise conferences, publish reports and submit memoranda to the government. Studies on these organisations and their mobilisation efforts are many (Ray 1986). We shall discuss the studies on human rights movements <strong>Participants:</strong> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Students and intellectuals have provided leadership to most middle-class movements. Though some of the populist, national and nativist movements draw support from peasants and other sections of society when they raise emotional and general issues; they continue to be dominated by the middle class. Myron Weiner observed, &lsquo;nativism is largely a middle-class sentiment, not a movement among the industrial labour force or the peasantry, even though there are culturally distinguishable migrants in the industrial labour force in many cities and in some rural areas&rsquo; (1978: 293). Some scholars argue that political leaders excite regional or nativist sentiments in the middle class for their political ends. Iqbal Narain asserts that the political elite exploits situations of regional deprivation and unrest and converts them into movements to forge and strengthen its individual and factional support bases (1984). While studying regionalism in Telengana, Ram Reddy and Sharma observed that factional politics exploited the regional sentiments of the people of Telengana for strengthening their political positions. Similarly, Subrarnanyam argues, Political leaders, when they feel that their due share is not received and they are being overshadowed and ignored, search for some kind of spontaneous rationale to infuse emotions among the people and project themselves as the protectors of public interests, and thus tensions and conflicts are created in an unparallel community in a democratic polity (1984:130). &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; However, Javed Alam propounds another theory. He argues, &lsquo;Re-gionalistic demands get flared up because of contradictions among the ruling classes.... The locally placed ruling classes seek greater power to further their own interests when such interests are perceived as not being served by the all India classes&rsquo; (1984: 17). He does not support his argument with evidence. As a result of their assumptions that these movements are created by the political elite, scholars do not examine the mobilisation aspect of the movement. They study primarily the decision-making process among the elite. Y.D. Phadke&rsquo;s study on the Samyukta Maharashtra movement (1979) is a case in point. Those who adhere to such conspiracy theories do not explain why political leaders succeed in arousing nativist emotions in certain states and why they fail in others. Most studies on middle-class movements discussed above are brief. Some deal with the political decision-making process and the factors responsible for the movement. Some of the movements were &lsquo;spontaneous&rsquo; and short-lived. They did not have an organisational structure, whereas some movements were well-organised. Many scholars do not analyse the organisational aspects of the movements. The studies on the Shiv Sena by Dipankar Gupta, the Nav Nirman and the Bihar movements by Ghan-shyam Shah and the Nav Nirman movement by P.M. Sheth, analyse the organisational structure of these movements. At this stage of our knowledge, it is difficult to find a pattern in organisational structures in different types of middle-class movements

In this paper, a new stemmer has been proposed named as “Root based stemmer”. This stemmer is strictly based on Dravidian script. Stemming can be used to pick up the effectiveness of information retrieval. In proposed Root based stemming technique, each and every token is compared against with all the words of a valid root words dictionary until a match is found. Then extract the matched string or substring from a token and identified as valid root. The present work is aimed to build dictionary based stemmer to extract valid root words for Indian languages especially for Telugu and compare the results with existing stemmers.

India is a multilingual multi-script country. In every state of India there are two languages one is state local language and the other is English. For example in Andhra Pradesh, a state in India, the document may contain text words in English and Telugu script. For Optical Character Recognition (OCR) of such a bilingual document, it is necessary to identify the script before feeding the text words to the OCRs of individual scripts. In this paper, we are introducing a simple and efficient technique of script identification for Kannada, English and Hindi text words of a printed document. The proposed approach is based on the horizontal and vertical projection profile for the discrimination of the three scripts. The feature extraction is done based on the horizontal projection profile of each text words. We analysed 700 different words of Kannada, English and Hindi in order to extract the discrimination features and for the development of knowledge base. We use the horizontal projection profile of each text word and based on the horizontal projection profile we extract the appropriate features. The proposed system is tested on 100 differentdocument images containing more than 1000 text words of each script and a classification rate of 98.25%, 99.25% and 98.87% is achieved for Kannada, English and Hindi respectively. &nbsp;

The need for offline handwritten character recognition is intense, yet difficult as the writing varies from person to person and also depends on various other factors connected to the attitude and mood of the person. However, we are able to achieve it by converting the handwritten document into digital form. It has been advanced with introducing convolutional neural networks and is further productive with pre-trained models which have the capacity of decreasing the training time and increasing accuracy of character recognition. Research in recognition of handwritten characters for Indian languages is less when compared to other languages like English, Latin, Chinese etc., mainly because it is a multilingual country. Recognition of Telugu and Hindi characters are more difficult as the script of these languages is mostly cursive and are with more diacritics. So the research work in this line is to have inclination towards accuracy in their recognition. Some research has already been started and is successful up to eighty percent in offline hand written character recognition of Telugu and Hindi. The proposed work focuses on increasing accuracy in less time in recognition of these selected languages and is able to reach the expectant values.

AbstractScript identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspaper and handwritten letters as well as notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given. The new multi-lingual database is expected to create new script identifiers, present various challenges, including identifying handwritten and printed samples and serve as a foundation for future research in script identification based on the reported results of the three benchmarks.

India is a large country with over a billion populations who speak numerous languages. 43% of Indians speak Devanagari Hindi script, followed by Bengali, Telugu, Marathi, and other languages. The widespread generation of content and accessibility would therefore greatly benefit from text-to-speech systems for such languages. In this research work we improve the already available Text-to-Speech (TTS) system using advance preprocessing techniques to the Hindi corpus database and applied various feature extraction techniques for better result. Finally we got the accuracy as 98% using MFCC and LPC feature extraction techniques. The developed model is capable for getting the input from audio file and read it loudly using developed TTS system.

In this work, we introduce WAFFNet, an attention-centric feature fusion architecture tailored for word-level multi-lingual scene text script identification. Motivated by the limitations of traditional approaches that rely exclusively on feature-based methods or deep learning strategies, our approach amalgamates statistical and deep features to bridge the gap. At the core of WAFFNet, we utilized the merits of Local Binary Pattern —a prominent descriptor capturing low-level texture features with high-dimensional, semantically-rich convolutional features. This fusion is judiciously augmented by a spatial attention mechanism, ensuring targeted emphasis on semantically critical regions of the input image. To address the class imbalance problem in multi-class classification scenarios, we employed a weighted objective function. This not only regularizes the learning process but also addresses the class imbalance problem. The architectural integrity of WAFFNet is preserved through an end-to-end training paradigm, leveraging transfer learning to expedite convergence and optimize performance metrics. Considering the under-representation of regional Indian languages in current datasets, we meticulously curated IIITG-STLI2023, a comprehensive dataset encapsulating English alongside six under-represented Indian languages: Hindi, Kannada, Malayalam, Telugu, Bengali, and Manipuri. Rigorous evaluation of the IIITG-STLI2023, as well as the established MLe2e and SIW-13 datasets, underscores WAFFNet’s supremacy over both traditional feature-engineering approaches as well as state-of-the-art deep learning frameworks. Thus, the proposed WAFFNet framework offers a robust and effective solution for language identification in scene text images.

Abstract In 1890, the Maldivian judge and poet Sheikh Muhammad Jamaluddin connected poetry with linguistic description in two ways. First, when he described features of the Dhivehi language with the aid of Arabic linguistic theory, he used Dhivehi poetry as linguistic evidence for correct usage. Second, he authored Dhivehi-language poetry about Arabic linguistic theory. Cosmopolis scholarship relates a narrative of how the wide circulation of Sanskrit, Arabic, and/or Persian fostered a vast network of writers who authored texts in major vernacular languages like Bengali, Burmese, Javanese, Kannada, Khmer, Malay, Sinhala, Tamil, Telugu, Thai, Tibetan, Turkish, and Urdu. This scholarship suggests that authors living within a particular cosmopolis wrote in divergent vernacular languages yet were, in some sense, connected because they translated and responded creatively to the same widely circulated source texts written in Sanskrit, Arabic, and/or Persian. Yet in cosmopolis scholarship's effort to reveal understudied connections, various degrees of disconnection among writers of vernacular languages within a cosmopolis tend to be missed. One problem of overlooking disconnection among writers of vernacular languages is that readers could mistakenly conflate superculture-subculture interaction with intercultural interaction. In this article, I argue that Dhivehi-language poetry and linguistic description was inside the Arabic cosmopolis but simultaneously outside, because in circa 1890 non-Maldivians in the Arabic cosmopolis of South and Southeast Asia could not even read the Thaana script of the Dhivehi language.

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

Abstrak Bagi siswa SMA yang mempelajari bahasa Jepang, penguasaan huruf Hiragana dianggap sebagai dasar yang sangat penting. Keterampilan ini memiliki signifikansi karena banyak kata-kata asli dalam bahasa Jepang, seperti nama tempat dan objek, ditulis dengan huruf Hiragana. Biasanya, pembelajaran Hiragana ditekankan sebelum mempelajari Katakana dan Kanji, terutama pada tahap awal pembelajaran di sekolah menengah atas. Penelitian ini menggunakan pendekatan quasi eksperimental sebagai metodologi. Metode ini dipilih karena sesuai dengan karakteristik sampel penelitian, yang terdiri dari kelompok pembanding atau kelompok kontrol dan kelompok eksperimen. Penentuan kelompok eksperimen dan kelompok kontrol sudah ditetapkan sebelumnya dan tidak dipilih secara acak, mengikuti desain nonequivalent control group (Sugiyono, 2014). Berdasarkan hasil analisis menggunakan Mann-Whitney Test, nilai Asymp. Sig (2-tailed) sebesar 0,000, yang lebih kecil dari 0,05. Ini mengindikasikan bahwa penggunaan Power Point Powtoon memiliki pengaruh signifikan terhadap kemampuan siswa kelas X di SMAN 1 Teluk Kuantan dalam menghafal huruf Hiragana. Kesimpulan dari penelitian ini adalah bahwa pemanfaatan media Power Point Powtoon memberikan dampak positif terhadap kemampuan siswa kelas X di SMAN 1 Teluk Kuantan dalam menghafal huruf Hiragana. Meskipun beberapa data tidak mengikuti distribusi normal, uji Mann-Whitney menunjukkan nilai signifikan, menegaskan bahwa dampak media tersebut cukup besar. Siswa yang menggunakan Powtoon menunjukkan peningkatan dalam mengartikulasikan huruf-huruf dan kemampuan menghafal huruf Hiragana. Top of Form Kata kunci: Hiragana, Power Point, Powtoon , Eksperimen. Abstract High school students studying Japanese are required to master the Hiragana script as a fundamental skill. This proficiency is crucial as native Japanese words, such as place names and objects, are written in Hiragana. Typically, the learning sequence prioritizes Hiragana before delving into Katakana and Kanji, especially in the early stages of high school. The research employed a quasi-experimental approach as its methodology. This method was chosen to align with the characteristics of the research sample, comprising a control group and an experimental group. The assignment of these groups was predetermined and not randomly selected, following a nonequivalent control group design (Sugiyono, 2014). Based on the findings from the Mann-Whitney Test, the Asymp. Sig (2-tailed) value was 0.000, which is smaller than 0.05. This indicates that the use of Power Point Powtoon has a significant impact on the ability of 10th-grade students at SMAN 1 Teluk Kuantan to memorize Hiragana characters. In conclusion, this study suggests that the utilization of Power Point Powtoon positively influences the proficiency of 10th-grade students at SMAN 1 Teluk Kuantan in memorizing Hiragana characters. Despite some data not following a normal distribution, the Mann-Whitney Test results show significant value, supporting the notion that the impact of the media is substantial. Students using Powtoon demonstrated improvement in articulating the characters and memorizing the Hiragana script. Keywords : Hiragana, Power Point, Powtoon , Experiment.

Teluk Jakarta merupakan pusat ekonomi maritim yang penting di Indonesia, berfungsi sebagai pusat perdagangan dan industri, serta memiliki nilai strategis dalam pertahanan nasional. Memantau perubahan salinitas laut sangat penting untuk menjaga stabilitas ekonomi dan keamanan maritim di wilayah ini. Penelitian ini bertujuan untuk memahami fluktuasi salinitas di Teluk Jakarta selama 20 tahun periode 1996 hingga 2020. Menggunakan data Global Ocean Physics Reanalysis diperoleh dari Copernicus Marine Environment Monitoring Service (CMEMS), data diolah dan dianalisis menggunakan metode FFT yang disusun sebagai bahasa pemrograman (script) Phyton. Hasilnya menunjukkan variasi tahunan dengan salinitas tertinggi pada tahun 2006 (31,5 hingga 33,0 psu) akibat penguapan dan penurunan aliran air tawar, sedangkan tahun 2010 mencatat salinitas terendah (31,00 hingga 31,75 psu) karena tingginya curah hujan. Klimatologis musiman, salinitas menurun selama musim hujan (November-April) dan meningkat selama musim kemarau (Mei-Oktober). Secara horizontal, salinitas lebih rendah di dekat pantai dan meningkat dengan kedalaman. Hasil analisis FFT siklus salinitas berulang setiap 10 tahun, mengindikasikan pengaruh variabilitas iklim jangka panjang. Rata-rata salinitas permukaan adalah 32,14 psu dengan sedikit fluktuasi tahunan. Distribusi salinitas harian berkisar antara 30,74 hingga 34,08 psu dengan rata-rata 32,31 psu dan standar deviasi 0,53 psu, menunjukkan konsistensi jangka panjang. Secara keseluruhan, meskipun terdapat variasi musiman dan siklus periodik, tren positif jangka panjang salinitas di Teluk Jakarta menunjukkan kenaikan meskipun kecil dari tahun ke tahun.