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Journal articles on the topic 'Devanagari script'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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1

Vijay, Vijay, M. U Kharat, and S. V Gumaste. "Study of Different Features and Classification Techniques for Recognition of Handwritten Devanagari Text." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1055. http://dx.doi.org/10.14419/ijet.v7i4.19.28285.

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Devanagari script is most popular and an older script in India. Millions of people all over the globe are using Devanagri script for various purposes such as communication, understanding the history, record keeping, research, etc. Recognition of handwritten Devanagari word is one of the popular area of research from decades because of its wide scope of applications. Different features and techniques of classification are the most important steps in the process of recognizing Devanagari handwritten word, are described in this paper.

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PANDEY, Krishna Kumar, and Smita JHA. "Tracing the Identity and Ascertaining the Nature of Brahmi-derived Devanagari Script." Acta Linguistica Asiatica 9, no. 1 (2019): 59–73. http://dx.doi.org/10.4312/ala.9.1.59-73.

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Current research exploits the orthographic design of Brahmi-derived scripts (also called Indic scripts), particularly the Devanagari script. Earlier works on orthographic nature of Brahmi-derived scripts fail to create a consensus among epigraphists, historians or linguists, and thus have been identified by various names, like semi-syllabic, subsyllabic, semi-alphabetic, alphasyllabary or abugida. On the contrary, this paper argues that Brahmi-derived scripts should not be categorized as scripts with overlapping features of alphabetic and syllabic properties as these scripts are neither alphabetic nor syllabic. Historical evolution and linguistic properties of Indic scripts, particularly Devanagari, ascertain the need for a new categorization of its own and, thus preferably merit a unique descriptor. This paper investigates orthographic characteristics of the Brahmi-derived Devanagari script, current trends in research pertaining to the Devanagari script along with other Indic scripts and the implications of these findings for literacy development in Indic writing systems.

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3

Ahmad, Rizwan. "Urdu in Devanagari: Shifting orthographic practices and Muslim identity in Delhi." Language in Society 40, no. 3 (2011): 259–84. http://dx.doi.org/10.1017/s0047404511000182.

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AbstractIn sociolinguistics, Urdu and Hindi are considered to be textbook examples of digraphia—a linguistic situation in which varieties of the same language are written in different scripts. Urdu has traditionally been written in the Arabic script, whereas Hindi is written in Devanagari. Analyzing the recent orthographic practice of writing Urdu in Devanagari, this article challenges the traditional ideology that the choice of script is crucial in differentiating Urdu and Hindi. Based on written data, interviews, and ethnographic observations, I show that Muslims no longer view the Arabic script as a necessary element of Urdu, nor do they see Devanagari as completely antithetical to their identity. I demonstrate that using the strategies of phonetic and orthographic transliteration, Muslims are making Urdu-in-Devanagari different from Hindi, although the difference is much more subtle. My data further shows that the very structure of a writing system is in part socially constituted. (Script-change, Urdu, Urdu-in-Devanagari, Hindi, Arabic script, Devanagari, orthography, transliteration)*

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Agnihotri, Ved Prakash. "Offline Handwritten Devanagari Script Recognition." International Journal of Information Technology and Computer Science 4, no. 8 (2012): 37–42. http://dx.doi.org/10.5815/ijitcs.2012.08.04.

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5

MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.

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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17

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Susan, Seba, and Jatin Malhotra. "Recognising Devanagari Script by Deep Structure Learning of Image Quadrants." DESIDOC Journal of Library & Information Technology 40, no. 05 (2020): 268–71. http://dx.doi.org/10.14429/djlit.40.05.16336.

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   Ancient Indic languages were written in the Devanagari script from which most of the modern-day Indic writing systems have evolved. The digitisation of ancient Devanagari manuscripts, now archived in national museums, is a part of the language documentation and digital archiving initiative of the Government of India. The challenge in digitizing these handwritten scripts is the lack of adequate datasets for training machine learning models. In our work, we focus on the Devanagari script that has 46 categories of characters that makes training a difficult task, especially when the number of samples are few. We propose deep structure learning of image quadrants, based on learning the hidden state activations derived from convolutional neural networks that are trained separately on five image quadrants. The second phase of our learning module comprises of a deep neural network that learns the hidden state activations of the five convolutional neural networks, fused by concatenation. The experiments prove that the proposed deep structure learning outperforms the state of the art.  

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7

Obaidullah, S. K., K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 81–94. http://dx.doi.org/10.4018/ijcvip.2017040106.

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Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used with three state-of-the-art features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations. The authors observed that MLP provides better results when all features are used, and achieved the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).

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Obaidullah, Sk Md, Chitrita Goswami, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Separating Indic Scripts with matra for Effective Handwritten Script Identification in Multi-Script Documents." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (2017): 1753003. http://dx.doi.org/10.1142/s0218001417530032.

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We present a novel approach for separating Indic scripts with ‘matra’, which is used as a precursor to advance and/or ease subsequent handwritten script identification in multi-script documents. In our study, among state-of-the-art features and classifiers, an optimized fractal geometry analysis and random forest are found to be the best performer to distinguish scripts with ‘matra’ from their counterparts. For validation, a total of 1204 document images are used, where two different scripts with ‘matra’: Bangla and Devanagari are considered as positive samples and the other two different scripts: Roman and Urdu are considered as negative samples. With this precursor, an overall script identification performance can be advanced by more than 5.13% in accuracy and 1.17 times faster in processing time as compared to conventional system.

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Malanker, Aradhana A., and Prof Mitul M Patel. "Handwritten Devanagari Script Recognition: A Survey." IOSR Journal of Electrical and Electronics Engineering 9, no. 2 (2014): 80–87. http://dx.doi.org/10.9790/1676-09228087.

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10

Kumar, Vijay, and Pankaj K. Sengar. "Segmentation of Printed Text in Devanagari Script and Gurmukhi Script." International Journal of Computer Applications 3, no. 8 (2010): 24–29. http://dx.doi.org/10.5120/749-1058.

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Sawant, U. M., R. K. Parkar, S. L. Shitole, and S. P. Deore. "Devanagari Script Recognition using Capsule Neural Network." International Journal of Computer Sciences and Engineering 7, no. 1 (2019): 208–11. http://dx.doi.org/10.26438/ijcse/v7i1.208211.

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12

Jayadevan, R., Satish R. Kolhe, Pradeep M. Patil, and Umapada Pal. "Offline Recognition of Devanagari Script: A Survey." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41, no. 6 (2011): 782–96. http://dx.doi.org/10.1109/tsmcc.2010.2095841.

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13

Prabhanjan, S., and R. Dinesh. "Deep Learning Approach for Devanagari Script Recognition." International Journal of Image and Graphics 17, no. 03 (2017): 1750016. http://dx.doi.org/10.1142/s0219467817500164.

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In this paper, we have proposed a new technique for recognition of handwritten Devanagari Script using deep learning architecture. In any OCR or classification system extracting discriminating feature is most important and crucial step for its success. Accuracy of such system often depends on the good feature representation. Deciding upon the appropriate features for classification system is highly subjective and requires lot of experience to decide proper set of features for a given classification system. For handwritten Devanagari characters it is very difficult to decide on optimal set of good feature to get good recognition rate. These methods use raw pixel values as features. Deep Learning architectures learn hierarchies of features. In this work, first image is preprocessed to remove noise, converted to binary image, resized to fixed size of 30[Formula: see text][Formula: see text][Formula: see text]40 and then convert to gray scale image using mask operation, it blurs the edges of the images. Then we learn features using an unsupervised stacked Restricted Boltzmann Machines (RBM) and use it with the deep belief network for recognition. Finally network weight parameters are fine tuned by supervised back propagation learning to improve the overall recognition performance. The proposed method has been tested on large set of handwritten numerical, character, vowel modifiers and compound characters and experimental results reveals that unsupervised method yields very good accuracy of (83.44%) and upon fine tuning of network parameters with supervised learning yields accuracy of (91.81%) which is the best results reported so far for handwritten Devanagari characters.

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14

Sinha, R. M. K. "Role of Context in Devanagari Script Recognition." IETE Journal of Research 33, no. 3 (1987): 86–91. http://dx.doi.org/10.1080/03772063.1987.11436670.

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15

Kapoor, Shuchi, and Vivek Verma. "Fragmentation of Handwritten Touching Characters in Devanagari Script." International Journal of Information Technology, Modeling and Computing 2, no. 1 (2014): 11–21. http://dx.doi.org/10.5121/ijitmc.2014.2102.

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Khanduja, Deepti, Neeta Nain, and Subhash Panwar. "A Hybrid Feature Extraction Algorithm for Devanagari Script." ACM Transactions on Asian and Low-Resource Language Information Processing 15, no. 1 (2016): 1–10. http://dx.doi.org/10.1145/2710018.

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17

Pradhan, Mukul, Madhumati Bajracharya, Manisha Rai, Piyush Man Shakya, Basudev KC, and Bishnu Prasad Bhusal. "Study Of Tremulous Signatures In Devanagari Nepali Script." Scientific World 14, no. 14 (2021): 99–105. http://dx.doi.org/10.3126/sw.v14i14.35020.

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Signatures, as one of the behavioral human characteristics are generally recognized as legal means for verifying an individual's identity by administrative and financial institutions. A signature is developed by a person and it can change over time. Signatures are prone to influences by age, physical and mental conditions. In this study tremulous signatures including questioned and standards related to 50 real cases resolved by National Forensic Science Laboratory of Nepal were collected. The time interval between questioned and standard signatures was up to ten years. Sixteen major characteristics along with their forty-one sub characteristics of each case were studied. Among sixteen major characteristics in the tremulous signatures, study shows that the characteristics like the style of writing in hand printed form, pen pauses, medium pen pressure, poor line quality, pen lifts are most prominently observed. Tremors were absent mostly in the terminal strokes of letters. The letters written were varied in size and those were 6% small, 70% medium and 24% large. Interestingly no any retouch attempts were found to complete the shape of letters.

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Sharma, Shubhankar, and Vatsala Arora. "Script Identification for Devanagari and Gurumukhi using OCR." International Journal of Computer Science and Mobile Computing 10, no. 9 (2021): 12–22. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.002.

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The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.

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Sproat, Richard. "Brahmi-derived scripts, script layout, and segmental awareness." Written Language and Literacy 9, no. 1 (2006): 45–66. http://dx.doi.org/10.1075/wll.9.1.05spr.

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In earlier work (Sproat 2000), I characterized the layout of symbols in a script in terms of a calculus involving two dimensional catenation operators: I claimed that leftwards, rightwards, upwards, downwards and surrounding catenation are sufficient to describe the layout of any script. In the first half of this paper I analyze four Indic alphasyllabaries — Devanagari, Oriya, Kannada and Tamil — in terms of this model. A crucial claim is that despite the complexities of layout in alphasyllabic scripts, they are essentially no different in nature than alphabetic scripts, such as Latin. The second part of the paper explores implications of this view for theories of phonology and human processing of orthography. Apparently problematic is evidence that “phonemic awareness” — the ability for literate speakers to manipulate sounds consciously at the phoneme level — is much stronger with alphabetic scripts, than with alphasyllabaries. But phonemic awareness is not categorically absent for readers of Indic scripts; in general, how aware a reader is of a particular phoneme is related to how that phoneme is rendered in the script. Relevant factors appear to include whether the symbol is written inline, whether it is a diacritic, and whether it is ligatured with another symbol.

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Moharkar, Lalita, Sudhanshu Varun, Apurva Patil, and Abhishek Pal. "A scene perception system for visually impaired based on object detection and classification using CNN." ITM Web of Conferences 32 (2020): 03039. http://dx.doi.org/10.1051/itmconf/20203203039.

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In this paper we have developed a system for visually impaired people using OCR and machine learning. Optical Character Recognition is an automated data entry tool. To convert handwritten, typed or printed text into data that can be edited on a computer, OCR software is used. The paper documents are scanned on simple systems with an image scanner. Then, the OCR program looks at the image and compares letter shapes to stored letter images. OCR in English has evolved over the course of half a century to a point that we have established application that can seamlessly recognize English text. This may not be the case for Indian languages, as they are much more complex in structure and computation compared to English. Therefore, creating an OCR that can execute Indian languages as suitably as it does for English becomes a must. Devanagari is one of the Indian languages spoken by more than 70% of people in Maharashtra, so some attention should be given to studying ancient scripts and literature. The main goal is to develop a Devanagari character recognition system that can be implemented in the Devanagari script to recognize different characters, as well as some words.

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Kaur, Amanpreet, Mohinder Singh, and Om Prakash Jasuja. "Interscript comparison of handwriting features leading to their identification and authorship." Nowa Kodyfikacja Prawa Karnego 45 (December 29, 2017): 15–36. http://dx.doi.org/10.19195/2084-5065.45.3.

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Identification of handwriting found on the disputed document by comparison with the known handwriting samples of the suspect still comprise the problem which is most commonly referred to a forensic document examiner. One of the important scientifically established principles which govern such analysis and identification is the ‘Principle of Comparison’ which explicitly states that, for obtaining correct results, like has to be compared with like; meaning thereby that the expert has to analyze and rely upon similar letters and combinations between the questioned and the standard handwriting samples and, consequently, the problems where similar handwriting samples in the same script have not been provided for comparison; usually fall outside the scope of forensic document examination. However, in this field, like any other human activity; perfect and ideal conditions are hard to achieve. Handwriting, being acquired skill and neuro-muscular controlled motor activity, its basic elements like the horizontal stroke, vertical stroke, loops, curves and arches etc., are combined together to form letters and alphabets of all the scripts. The question then arises — whether inter-script comparison of handwriting samples can be attempted leading to some limited or qualified conclusions. Thus, if it becomes possible and practicable to examine and compare the basic elements of questioned handwriting in one script, say Devanagri with the similar elements found in specimen/ admitted handwriting samples in another script by the same writer, say Gurmukhi, because sample handwritings in Devanagri could not be procured for whatsoever reasons; the scope of examination can be widened further and expert may be in a position to express some opinion regarding their common authorship or otherwise, which may be found worthwhile to the investigating agency or the court of law, thereby helping in the administration of justice ultimately.To the best of our knowledge, not much research is available, where writings produced in different scripts by the same writer could be compared, thereby leading to a definite opinion on the issue of their common authorship or otherwise. In the present study, an attempt has been made to explore this issue by taking writing samples of the same writer in three scripts, having knowledge of all the three commonly used languages, i.e., English, Hindi, and Punjabi, corresponding to the said scripts i.e., Roman, Devanagari and Gurumukhi. Three hundred sixty 360 writing samples were obtained from as many as 40 individuals appropriately skilled in writing, reading and speaking these languages/ scripts. Careful study and evaluation of the basic elements of written strokes whose execution were found to be similar in all the three scripts has been carried out indicating the possibility of ‘Script Independent Comparison’. Limitations of the proposed study have also been discussed in the paper.

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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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CHOKSI, NISHAANT. "From Language to Script: Graphic practice and the politics of authority in Santali-language print media, eastern India." Modern Asian Studies 51, no. 5 (2017): 1519–60. http://dx.doi.org/10.1017/s0026749x16000470.

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AbstractThis article discusses the way in which assemblages of technologies, political institutions, and practices of exchange have rendered both language and script a site for an ongoing politics of authority among Santals, an Austro-Asiatic speaking Adivasi (Scheduled Tribe) community spread throughout eastern India. It focuses particularly on the production of Santali-language print artefacts, which, like its dominant language counterparts, such as Bengali, has its roots in colonial-era Christian missions. However, unlike dominant languages, Santali-language media has been characterized by the use of multiple graphic registers, including a missionary-derived Roman script, Indic scripts such as Devanagari and Eastern Brahmi, and an independently derived script, Ol-Chiki. The article links the history of Santali print and graphic practice with assertions of autonomy in colonial and early post-colonial India. It then ethnographically documents how graphic practices, in particular the use of multiple scripts, and print technologies mediate a contemporary politics of authority along vectors such as class and generation within communities that speak and read Santali in the eastern state of West Bengal, India.

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G., Amogh, Baasit Sharief, and Omkar B. "Measuring Performance of Generative Adversarial Networks on Devanagari Script." International Journal of Computer Applications 176, no. 33 (2020): 5–9. http://dx.doi.org/10.5120/ijca2020920393.

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Thapliya, Roshan, Hirokazu Koizumi, Kashiko Kodate, and Takeshi Kamiya. "Parallel joint transform correlator applied to Devanagari script recognition." Applied Optics 37, no. 23 (1998): 5408. http://dx.doi.org/10.1364/ao.37.005408.

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26

Yadav, Pooja, and Sonia Sharma. "A Review on Various Techniques of Devanagari Script Recognition." International Journal of Engineering Trends and Technology 33, no. 6 (2016): 257–64. http://dx.doi.org/10.14445/22315381/ijett-v33p251.

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Yadav, Pooja, and Sonia Sharma. "Enhancing Performance of Devanagari Script Recognition using Hopfield ANN." International Journal of Engineering Trends and Technology 36, no. 2 (2016): 66–75. http://dx.doi.org/10.14445/22315381/ijett-v36p213.

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KantYadav, Ravi, and Bireshwar Dass Mazumdar. "Detection of Bold and Italic Character in Devanagari Script." International Journal of Computer Applications 39, no. 2 (2012): 19–22. http://dx.doi.org/10.5120/4792-7037.

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Thapliya, Roshan, Masahiro Tsuchiya, and Takeshi KAmiya. "Joint transform correlator applied to recognition of Devanagari script." Optical Review 3, no. 6 (1996): A397—A399. http://dx.doi.org/10.1007/bf02935942.

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30

SOHONI, PUSHKAR. "Marathi of a Single Type: The demise of the Modi script." Modern Asian Studies 51, no. 3 (2016): 662–85. http://dx.doi.org/10.1017/s0026749x15000542.

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AbstractWhile the debates about the use of a single script for rendering the Marathi language became relevant only after the advent of printing, the fast-changing social and political landscapes of the nineteenth and twentieth centuries lent their own weight to the discourse. The debates about the writing system became the venue for various competing social forces and political movements. The issues of region, caste, class, and religion—the core of today's identity politics—were all embroiled in this debate, as were both the British colonial and Indian nationalist governments. In just 150 years, Balbodh (a variant of Devanagari) emerged as the sole script for the Marathi language. At least three different arguments were used to dismiss the Modi script. The first was about printing types, and the legibility and economy of Devanagari. By the end of the nineteenth century, the social empowerment of the literati and administrative convenience were the reasons given for abolishing Modi. In the twentieth century, British resistance to nationalist efforts in western India, and then a fear of regionalism under the new nationalist independent republic, ensured that a single script able to be used for both Hindi and Sanskrit would be officially sanctioned for Marathi.

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Line Parameter based Word-Level Indic Script Identification System." International Journal of Computer Vision and Image Processing 6, no. 2 (2016): 18–41. http://dx.doi.org/10.4018/ijcvip.2016070102.

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In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Manipuri, Oriya, Urdu, and Roman. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentations are performed at word-level using multiple classifiers on a dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%. The performance of the present technique is also compared with those of other state-of-the-art script identification methods on the same database. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentation are performed at word-level on a total dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%.

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de Voogt, Alexander J. "The Meroitic script and the understanding of alpha-syllabic writing." Bulletin of the School of Oriental and African Studies 73, no. 1 (2010): 101–5. http://dx.doi.org/10.1017/s0041977x0999036x.

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AbstractAt the time of its decipherment by Griffith (1911), the Meroitic writing system was considered an alphabet. This alphabet was found to have a rather limited vowel notation. It was not until 1970 that the system was understood to have a more complex vowel notation. This system of vowel notation is comparable to what is found in an alpha-syllabary, a term used to describe the scripts of the Indian sub-continent, such as Brahmi and Devanagari. Since alpha-syllabaries were rare when the Meroitic writing system was in use (c. 200 bce–c. 500 ad), it is tempting to suggest a possible historical connection between the Meroitic kingdom in Sudan and the then existent scripts in India. A systematic analysis, as opposed to a description of alpha-syllabic writing, indicates that the structure of this type of script is less regionally confined. Rather, it places Meroitic writing among scripts that were created in the presence of alphabetic writing both in modern and in ancient times.

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Govilkar, Sharvari S., J. W. Bakal, and Sagar R. Kulkarni. "Extraction of Root Words using Morphological Analyzer for Devanagari Script." International Journal of Information Technology and Computer Science 8, no. 1 (2016): 33–39. http://dx.doi.org/10.5815/ijitcs.2016.01.04.

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Kolte, Gauri, Jennifer Fernandes, Prashant Vishwakarma, Samira Nikharge, and Shalaka Deore. "A Survey on Recent Advances to Read Handwritten Devanagari Script." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 589–95. http://dx.doi.org/10.26438/ijcse/v7i2.589595.

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Jundale, Trupti A., and Ravindra S. Hegadi. "Skew Detection and Correction of Devanagari Script Using Hough Transform." Procedia Computer Science 45 (2015): 305–11. http://dx.doi.org/10.1016/j.procs.2015.03.147.

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Gupta, Vidhu, and Neeraj Jain. "Frequency Analysis Of Speech Signals For Devanagari Script And Numerals." IOSR Journal of Electronics and Communication Engineering 11, no. 05 (2016): 124–29. http://dx.doi.org/10.9790/2834-110501124129.

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Jindal, Khushneet, and Rajiv Kumar. "A Novel Shape-Based Character Segmentation Method for Devanagari Script." Arabian Journal for Science and Engineering 42, no. 8 (2017): 3221–28. http://dx.doi.org/10.1007/s13369-017-2420-7.

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Guha, Ritam, Manosij Ghosh, Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri. "M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification." Journal of Intelligent Systems 29, no. 1 (2019): 1453–67. http://dx.doi.org/10.1515/jisys-2019-0064.

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Abstract The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm (M-HMOGA) is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and Regional Weighted Run Length features. The experimentations are carried out on Bangla, Devanagari, and Roman numeral datasets, which are the three most popular scripts used in the Indian subcontinent. In-house Bangla and Devanagari script datasets and Competition on Handwritten Digit Recognition (HDRC) 2013 Roman numeral dataset are used for evaluating our model. Moreover, as proof of robustness, we have applied an innovative approach of using different datasets for training and testing. We have used in-house Bangla and Devanagari script datasets for training the model, and the trained model is then tested on Indian Statistical Institute numeral datasets. For Roman numerals, we have used the HDRC 2013 dataset for training and the Modified National Institute of Standards and Technology dataset for testing. Comparison of the results obtained by the proposed model with existing HMOGA and MOGA techniques clearly indicates the superiority of M-HMOGA over both of its ancestors. Moreover, use of K-nearest neighbor as well as multi-layer perceptron as classifiers speaks for the classifier-independent nature of M-HMOGA. The proposed M-HMOGA model uses only about 45–50% of the total feature set in order to achieve around 1% increase when the same datasets are partitioned for training-testing and a 2–3% increase in the classification ability while using only 35–45% features when different datasets are used for training-testing with respect to the situation when all the features are used for classification.

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Dubey, Namrata, Naoko Witzel, and Jeffrey Witzel. "Script differences and masked translation priming: Evidence from Hindi-English bilinguals." Quarterly Journal of Experimental Psychology 71, no. 11 (2018): 2421–38. http://dx.doi.org/10.1177/1747021817743241.

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This study reports on two experiments investigating the effects of script differences on masked translation priming in highly proficient early Hindi-English bilinguals. In Experiment 1 (the cross-script experiment), L1 Hindi was presented in the standard Devanagari script, while L2 English was presented in the Roman alphabet. In Experiment 2 (the same-script experiment), both L1 Hindi and L2 English were presented in the Roman alphabet. Both experiments revealed translation priming in the L1-L2 direction. However, L2-L1 priming was obtained in the same-script experiment, but not in the cross-script experiment. These findings are discussed in relation to the orthographic cue hypothesis as well as hypotheses that hold that script differences influence the distance between the L1 and L2 in lexical space and/or cross-language lateral inhibition. We also provide alternative accounts for these results in terms of how orthographic cues provided by L1 targets might lead to the discontinuation or disruption of processing for L2 primes.

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Ghosh, Rajib, Partha Pratim Roy, and Prabhat Kumar. "Smart Device Authentication Based on Online Handwritten Script Identification and Word Recognition in Indic Scripts Using Zone-Wise Features." International Journal of Information System Modeling and Design 9, no. 1 (2018): 21–55. http://dx.doi.org/10.4018/ijismd.2018010102.

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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.

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Pal Godara, Savita, and Pratap Singh Patwal. "Latin Script Detection and Removal from Devanagari Document Image for OCR." International Journal of Computer & Organization Trends 6, no. 1 (2014): 33–36. http://dx.doi.org/10.14445/22492593/ijcot-v6p308.

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Sharma, Reya, Baij Nath Kaushik, and Naveen Kumar Gond. "Devanagari and Gurmukhi Script Recognition in the Context of Machine Learning Classifiers." Journal of Artificial Intelligence 11, no. 2 (2018): 65–70. http://dx.doi.org/10.3923/jai.2018.65.70.

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Vaidya, Madhav, Yashwant Joshi, and Milind Bhalerao. "Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform." International Journal of Intelligent Engineering and Systems 10, no. 6 (2017): 78–86. http://dx.doi.org/10.22266/ijies2017.1231.09.

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Shete, D. S., Prof S.B. Patil, and Prof S.B. Patil. "Zero crossing rate and Energy of the Speech Signal of Devanagari Script." IOSR journal of VLSI and Signal Processing 4, no. 1 (2014): 01–05. http://dx.doi.org/10.9790/4200-04110105.

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Kshirsagar, G. B., and N. D. Londhe. "Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning." IEEE Transactions on Biomedical Engineering 66, no. 11 (2019): 2992–3005. http://dx.doi.org/10.1109/tbme.2018.2875024.

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Vaid, Jyotsna, and Ashum Gupta. "Exploring Word Recognition in a Semi-Alphabetic Script: The Case of Devanagari." Brain and Language 81, no. 1-3 (2002): 679–90. http://dx.doi.org/10.1006/brln.2001.2556.

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Shiravale, Sankirti Sandeep, Jayadevan R, and Sanjeev S. Sannakki. "Recognition of Devanagari Scene Text Using Autoencoder CNN." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (2021): 55–69. http://dx.doi.org/10.5565/rev/elcvia.1344.

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Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.

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Singh, Pawan Kumar, Ram Sarkar, Nibaran Das, Subhadip Basu, Mahantapas Kundu, and Mita Nasipuri. "Benchmark databases of handwritten Bangla-Roman and Devanagari-Roman mixed-script document images." Multimedia Tools and Applications 77, no. 7 (2017): 8441–73. http://dx.doi.org/10.1007/s11042-017-4745-3.

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Holambe, Anilkumar N., Dr Ravinder C. Thool, and Dr S. M. Jagade. "Printed and Handwritten Character & Number Recognition of Devanagari Script using gradient features." International Journal of Computer Applications 2, no. 9 (2010): 38–41. http://dx.doi.org/10.5120/692-972.

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Mullaney, Thomas S. "The Font that Never Was: Linotype and the “Phonetic Chinese Alphabet” of 1921." Philological Encounters 3, no. 4 (2018): 550–66. http://dx.doi.org/10.1163/24519197-12340049.

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Abstract Since the invention and globalization of hot metal printing in the United States and Europe, engineers and entrepreneurs dreamt of a day when linotype and monotype technologies would absorb Chinese script into its growing repertoire of non-Latin writing systems, just as they had Arabic, Armenian, Burmese, Devanagari, Hebrew, Korean, and over one hundred other scripts. In the early 1920s, the much-celebrated release of a new font—the “Chinese Phonetic Alphabet” by Mergenthaler Linotype, and later by the Monotype corporation—led many to believe that the day had finally come. This article charts out the quixotic history of Linotype and Monotype’s efforts to enter the Chinese market, examining the linguistic challenges that had long prevented China’s absorption into a Western-dominated “hot metal empire,” the design process by which artists in Brooklyn and London crafted these new fonts, and ultimately the cultural misunderstandings that doomed the projects to failure.

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