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Academic literature on the topic 'Telugu Script'

Author: Grafiati

Published: 5 June 2025

Last updated: 11 July 2025

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Journal articles on the topic "Telugu Script"

1

S, Govindammal. "Characteristics of Telugu in Middle Dravidian Languages." International Research Journal of Tamil 4, S-5 (2022): 12–16. http://dx.doi.org/10.34256/irjt22s52.

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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.

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Srinivasa Rao, Adabala Venkata, D. R. Sandeep, V. B. Sandeep, and S. Dhanam Jaya. "Segmentation of Touching Hand written Telugu Characters by using Drop Fall Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (2012): 343–46. http://dx.doi.org/10.24297/ijct.v3i2c.2897.

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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.

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Behera, Suryosnata, and Dr SatyaRanjan Pattanaik. "Recognition And Classification of Indian Scripts in Natural Scene Images." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36661.

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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

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Padmavathi Pragada. "Automated LSTM Based Deep Learning Model for Handwritten Telugu Answer Script Analysis." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 745–62. https://doi.org/10.52783/cana.v32.3796.

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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.

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Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Word-Level Script Identification Using Texture Based Features." International Journal of System Dynamics Applications 4, no. 2 (2015): 74–94. http://dx.doi.org/10.4018/ijsda.2015040105.

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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).

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Boddu, Rajasekhar, and Edara Sreenivasa Reddy. "Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (2023): 296–305. http://dx.doi.org/10.17762/ijritcc.v11i4s.6567.

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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.

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Chaudhuri, B. B., O. A. Kumar, and K. V. Ramana. "Automatic Generation and Recognition of Telugu Script Characters." IETE Journal of Research 37, no. 5-6 (1991): 499–511. http://dx.doi.org/10.1080/03772063.1991.11437004.

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R, Mr Venkatesh. "Handwritten Telugu Character Recognition & Signature Verification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31955.

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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.

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Subrahmanyam, Mslb, V. Vijaya Kumar, and B. Eswara Reddy. "A novel method for segmenting and straightening of text lines in handwritten Telugu documents based on smearing and regression approach." International Journal of Engineering & Technology 7, no. 3 (2018): 1846. http://dx.doi.org/10.14419/ijet.v7i3.13286.

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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.

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., N. Swapna. "RULE BASED PSEUDO N-GRAM MODEL FOR TELUGU SCRIPT." International Journal of Research in Engineering and Technology 06, no. 01 (2017): 8–12. http://dx.doi.org/10.15623/ijret.2017.0601002.

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Books on the topic "Telugu Script"

1

Sastry, P. V. Parabrahma. Telugu lipi, āvirbhāva vikāsālu =: Telugu script : origin and evolution. Āndhrapradēś Prabhutva Prācyalikhita Granthālayaṃ mariyu Pariśōdhanālayaṃ, 2012.

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2

Ramachandra Murthy, N. S., 1943-, Tirumalarāvu Jayadhīr, and Andhra Pradesh Government Oriental Manuscripts Library and Research Institute, eds. Telugu script: Origin and evolution (3rd c. BC - 16th c. AD). Andhra Pradesh Government Oriental Manuscripts Library & Research Institute, 2009.

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3

Govinda Rao, T. K., 1929-, ed. Compositions of Tyāgarāja in national and international scripts, Dēvanāgari & Roman with meaning and SRGM notations in English. 2nd ed. Ganamandir Publications, 1999.

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4

Raghavan, V. (Venkatarama), 1908-1979, editor, Akbar Shah, Akbar Shah та Ṭākṭar Vē. Rākavan̲ Nikal̲ Kalaikaḷ Maiyam, ред. Śr̥ṅgāramañjarī of Saint Akbar Shah: Based on Sanskrit Manuscripts in Devanagari and Telugu scripts ; revised edition with edditional readings. Dr. V. Raghavan Centre for Performing Arts, 2012.

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5

Kalaya Nijama telugu movie script: Complete telugu movie script. Maheshh, 2018.

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6

Build, Mount. Site Book: A Conception in Telugu Script from Ancient Artifacts. DrumWork, 2020.

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Gmbh, Babadada. BABADADA, Telugu - Burmese , visual dictionary - visual dictionary: ... script), visual dictionary. Babadada, 2020.

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Gmbh, Babadada. BABADADA black-and-white, Sindhi - Telugu , visual dictionary - visual dictionary ... script), visual dictionary. Babadada, 2020.

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Gmbh, Babadada. BABADADA, Nederlands met lidwoorden - Telugu , het beeldwoordenboek - visual dictionary: Dutch with articles - ... script), visual dictionary. Babadada, 2020.

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Gmbh, Babadada. BABADADA, Español con articulos - Telugu , el diccionario visual - visual dictionary: Spanish with articles - ... script), visual dictionary. Babadada, 2020.

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Book chapters on the topic "Telugu Script"

1

Aalla, Chakradhar, and Mohammad Shahid. "Impact of Writing Tools in the Evolution of Telugu Script." In Ergonomics for Design and Innovation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94277-9_29.

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Aalla, Chakradhar, and Mohammad Shahid. "Impact of Writing Tools in the Evolution of Telugu Script." In Ergonomics for Design and Innovation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94277-9_29.

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Mohana Lakshmi, K., and T. Ranga Babu. "A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0212-1_58.

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Swamy Das, M., Kovvur Ram Mohan Rao, and P. Balaji. "Neural-Based Hit-Count Feature Extraction Method for Telugu Script Optical Character Recognition." In Lecture Notes in Networks and Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8204-7_48.

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Hebbi, Chandravva, H. R. Mamatha, Y. S. Sahana, Sagar Dhage, and Shriram Somayaji. "A Convolution Neural Networks Based Character and Word Recognition System for Similar Script Languages Kannada and Telugu." In Proceedings of ICETIT 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_26.

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Dhandra, B. V., Satishkumar Mallappa, and Gururaj Mukarambi. "Script Identification of Camera Based Bilingual Document Images Using SFTA Features." In Research Anthology on Bilingual and Multilingual Education. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3690-5.ch040.

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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.

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"TELUGU." In The Routledge Handbook of Scripts and Alphabets. Routledge, 2003. http://dx.doi.org/10.4324/9780203169483-37.

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Conference papers on the topic "Telugu Script"

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Kinjarapu, Ananda Kumar, Kalyan Chakravarti Yelavarti, and Kamakshi Prasad Valurouthu. "Online recognition of handwritten Telugu script characters." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955866.

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Jayaraman, A., C. C. Sekhar, and V. S. Chakravarthy. "Modular Approach to Recognition of Strokes in Telugu Script." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4378760.

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Lakshmi, K. Mohana, and T. Ranga Babu. "Searching for TELUGU Script in Noisy Images Using SURF descriptors." In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.95.

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Patil, Kishor, Neha Gupta, Damodar M, and Ajai Kumar. "Towards Modi Script Preservation: Tools for Digitization." In 12th International Conference on Computer Science and Information Technology (CCSIT 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121305.

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Modi (मोडी, modī ̣) is a heritage script belonging to Brahmi family, which is used mainly for writing Marathi, an Indo-Aryan language spoken in western and central India, mostly in the state of Maharashtra. “Modi-manuscript "written from the past, reveals the history of the Maratha Empire from its inception under Chhatrapati Shivaji Maharaj; to the creation of movable metal type when Modi was slowly relegated to an inferior position, unfolds perspectives and reflects the social, political and cultural sense of his time." Today it is very important for historians, researchers and students to understand this script and use it for historical heritage. Other regional languages such as Hindi, Gujarati, Kannada, Konkani and Telugu were also using Modi. This paper presents our contribution in helping the community for preserving the script, by way of using various tools, which will facilitate the collection, analysis, and digitization of the Modi script.

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Gadde, Chris Andrew, Santhoshini Reddy, Viswanath Pulabaigari, and Umapada Pal. "Text Independent Writer Identification for Telugu Script Using Directional Filter Based Features." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. http://dx.doi.org/10.1109/icdar.2017.330.

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Rajkumar, J., K. Mariraja, K. Kanakapriya, S. Nishanthini, and V. S. Chakravarthy. "Two Schemas for Online Character Recognition of Telugu Script Based on Support Vector Machines." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.286.

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Padma, M. C., and P. A. Vijaya. "Monothetic separation of Telugu, Hindi and English text lines from a multi script document." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346045.

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Purushotham Reddy, M., Vss Sreekar, M. Srikanth, and K. Siddartha. "Text Summarization of Telugu Scripts." In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2021. http://dx.doi.org/10.1109/i-smac52330.2021.9640729.

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Ashlin, Deepa R. N., Y. Vijayalata, and Atul Negi. "Document Text Analysis and Recognition of Handwritten Telugu Scripts." In 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). IEEE, 2022. http://dx.doi.org/10.1109/icccmla56841.2022.9989012.

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Rajashekararadhya, S. V., and P. Vanaja Ranjan. "Neural network based handwritten numeral recognition of Kannada and Telugu scripts." In TENCON 2008 - 2008 IEEE Region 10 Conference (TENCON). IEEE, 2008. http://dx.doi.org/10.1109/tencon.2008.4766450.

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