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Relevant bibliographies by topics / Indic Script

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Academic literature on the topic 'Indic Script'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Indic Script"

1

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 alphab

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S, K. Shreesha, and B. Anita H. "Classification of North and South Indian Handwritten Scripts using Gabor Wavelet Features." Indian Journal of Science and Technology 15, no. 16 (2022): 712–17. https://doi.org/10.17485/IJST/v15i16.88.

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Abstract <strong>Objectives:</strong> Handwritten script identification plays a vital role in processing handwritten data electronically. Most of the methods fail to provide accuracy due to variation in handwriting, hence the classification of the Indic script before providing it to OCR is crucial. The anticipated work helps increase the accuracy by categorizing the handwritten documents as north or South Indic script before further classification. <strong>Methods:</strong> This study has proposed a method, using Gabor filters to extract features from the text image for recogniz

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Singh, Pawan Kumar, Ram Sarkar, Ajith Abraham, and Mita Nasipuri. "A Case Study on Handwritten Indic Script Classification: Benchmarking of the Results at Page, Block, Text-line, and Word Levels." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (2022): 1–36. http://dx.doi.org/10.1145/3476102.

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Handwritten script classification is still considered as a challenging research problem in the domain of document image analysis. Although some research attempts have been made by the researchers for solving the challenging issues, a comprehensive solution is yet to be achieved. The case study, undertaken here, analyzes the performances of various state-of-the art handwritten script classification methods for Indian scripts where features, needed for the script classification task, are extracted from the script images at four different granularity levels, i.e., page, block, text line, or word.

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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 sec

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Obaidullah, Sk Md, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Handwritten Indic Script Identification in Multi-Script Document Images: A Survey." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 10 (2018): 1856012. http://dx.doi.org/10.1142/s0218001418560128.

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Script identification is crucial for automating optical character recognition (OCR) in multi-script documents since OCRs are script-dependent. In this paper, we present a comprehensive survey of the techniques developed for handwritten Indic script identification. Different pre-processing and feature extraction techniques, including classifiers used for script identification, are categorized and their merits and demerits are discussed. We also provide information about some handwritten Indic script datasets. Finally, we highlight the extensions and/or future scope of works together with challe

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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 scri

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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 handwritt

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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

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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 wh

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Researcher. "COMPARING MULTILINGUAL LANGUAGE MODELS ON INDIC NEWS HEADLINE CLASSIFICATION." International Journal of Artificial Intelligence Research and Development (IJAIRD) 2, no. 2 (2024): 122–28. https://doi.org/10.5281/zenodo.14190096.

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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

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Dissertations / Theses on the topic "Indic Script"

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Thomas, Sandhya Achamma. "Navigating A World In Flux: Sexual Scripts In India." Oxford, Ohio : Miami University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1228500236.

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LeBlanc, Paul D. "Indus Epigraphic Perspectives: Exploring Past Decipherment Attempts & Possible New Approaches." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/26166.

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First appearing on potsherds around 3300 BC, the Indus script was primarily in use during the Mature Harappan period (ca. 2600-1900 BC) in the Indus Valley region, centred in the north-western region of the Indian Subcontinent. It is one of the last remaining undeciphered scripts of the ancient world. A great number of Indus inscriptions, however, have been uncovered at many archaeological sites in the Persian Gulf, discoveries that corroborate the inclusion of the Indus civilization as an active participant in the Mesopotamian-dominated Gulf trade of the 3rd millennium. In addition to explori

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Books on the topic "Indic Script"

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Newberry, John. Indus seal-script of Lothat. John Newberry, 1986.

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Brigitta, Kook, and Wirth Marianne, eds. Südostasiatische Küche: [Sanscrit script] : Indische Küche. 3rd ed. Asfahani, 1993.

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Newberry, John. Indus script & Proto-Elamite: The logical subject. J. Newberry, 1987.

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K, Sharma R., ed. Studies in the Shell Script. Agam Kala Prakashan, 1990.

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Newberry, John. Indus copper tablets from Mohenjo-Daro. J. Newberry, 1985.

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Zaidi, Shamshad. An introduction to Urdu script: Reading and writing. Central Institute of Indian Languages, 1997.

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Nazir, Hassan, and Central Institute of Indian Languages., eds. An introduction to Urdu script: Reading and writing. Central Institute of Indian Languages, 1997.

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8

Aryan, K. C. Encyclopedia of Indian art, references, symbols, evolution of Devanagari script =: Rekhā. Rekha Prakashan, 1989.

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9

Okell, John. Burmese: An introduction to the script. Center for Southeast Asian Studies, Northern Illinois University, 1994.

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Newberry, John. The Egyptian-Indus war, 1850 B.C. J. Newberry], 1994.

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Book chapters on the topic "Indic Script"

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Obaidullah, Sk Md, Chitrita Goswami, K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Separating Indic Scripts with ‘matra’—A Precursor to Script Identification in Multi-script Documents." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2104-6_19.

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Ghosh, Subhasmita, Ashif Sheikh, Sk Golam Sarowar Hossain, et al. "Handwritten Indic Script Identification – A Multi-level Approach." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8581-0_9.

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Senapati, Deepak, Abinash Panda, Siddhant Mund, and Sunil Kumar Mohapatra. "Descriptive Indic Answer Script Evaluation Using Deep Learning." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0749-3_69.

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De, Sreayan, Sujit Kumar Mondal, and Pawan Kumar Singh. "Handwritten Indic Script Recognition Using Deep Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5412-0_41.

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "A Two-Stage Approach to Handwritten Indic Script Identification." In Hybrid Intelligence for Image Analysis and Understanding. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119242963.ch3.

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Garain, Utpal, Mangal P. Chakraborty, and Dipankar Dasgupta. "Recognition of Handwritten Indic Script Using Clonal Selection Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823940_20.

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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Recognition of Handwritten Indic Script Numerals Using Mojette Transform." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2035-3_47.

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Chakraborty, Saikat, Riktim Mondal, Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri. "Genetic Dropout: An Application to Handwritten Indic Script Classification." In Advanced Techniques for IoT Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4435-1_1.

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Ukil, Soumya, Swarnendu Ghosh, Sk Md Obaidullah, K. C. Santosh, Kaushik Roy, and Nibaran Das. "Deep Learning for Word-Level Handwritten Indic Script Identification." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0507-9_42.

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Obaidullah, Sk Md, K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Thirteen Official Indic Languages Database for Script Identification in Multi-script Documents." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4859-3_2.

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Conference papers on the topic "Indic Script"

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Gupta, Akant Mohan, and Harmohan Sharma. "Text Recognition in Indic Scripts using Deep Learning - A Comprehensive Analysis." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934495.

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Singh, Pawan Kumar, Ram Sarkar, Mita Nasipuri, and David Doermann. "Word-level script identification for handwritten Indic scripts." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333932.

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Pati, P., and A. Ramakrishnan. "A Blind Indic Script Recognizer for Multi-script Documents." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4377115.

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Bhargava, Rupal, Yashvardhan Sharma, and Shubham Sharma. "Sentiment analysis for mixed script Indic sentences." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732099.

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Reddy, P. Vijayapal, B. Vishnu Vardhan, and A. Govardhan. "Corpus Based Extractive Document Summarization for Indic Script." In 2011 International Conference on Asian Language Processing (IALP). IEEE, 2011. http://dx.doi.org/10.1109/ialp.2011.66.

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Singh, Rajwinder, and Charanjiv Singh Saroa. "Multilingual Conversation ASCII to Unicode in Indic Script." In Sixth International Conference on Information Technology Convergence and Services. Academy & Industry Research Collaboration Center (AIRCC), 2017. http://dx.doi.org/10.5121/csit.2017.70308.

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Malsattar, Nirav, Nagraj Emmadi, and Manjiri Joshi. "Testing the efficacy of an Indic script virtual keyboard." In the India HCI 2014 Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2676702.2677203.

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Sharma, Debraj Rudra, and Sreebha Bhaskaran. "Categorization of Multilingual Text on Languages of Indic Script." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978281.

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Gaur, Shivansh, Siddhant Sonkar, and Partha Pratim Roy. "Generation of synthetic training data for handwritten Indic script recognition." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333810.

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Garain, U., M. P. Chakraborty, and B. Chanda. "Lossless Compression of Textual Images: A Study on Indic Script Documents." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.776.

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Reports on the topic "Indic Script"

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Rene, Schubert. Computing the Meridional Overturning Circulation from NEMO Output. GEOMAR, 2021. http://dx.doi.org/10.3289/sw_3_2021.

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With this script, the Meridional Overturning Circulation (MOC) can be computed from NEMO ocean-model output for the whole globe or the Atlantic (AMOC), Indic (IMOC) and Pacific (PMOC) subbasins. The MOC is computable in z- and sigma coordinates. Moreover, for nested configurations, it is possible to combine data from both host and nest grids. Finally, it is possible to take into account of that the ORCA model grid is curvilinear north of 20°N: it is possible to compute the northward velocity component from the velocity field in x- and y- directions and to sum up the meridional flux over latitu

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