Relevant bibliographies by topics / Tamil script

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Tamil script'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Tamil script"

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Jani, Meenakshi. "State of Emergence." Columbia Journal of Asia 1, no. 1 (April 26, 2022): 188–206. http://dx.doi.org/10.52214/cja.v1i1.9318.

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During the Sri Lankan Civil War, an entire generation grew up under a condition of violence. I analyze the violence of the Sri Lankan Civil War as a destabilizing as well as productive force, rather than only as the result of pre-existing social conflicts. I argue that the condition of violence during this war lent itself to new iterations of Tamil ethnic identity, focusing on how it created space for the renegotiation of Tamil womanhood. Inspired by Lee Ann Fuji’s conceptualization of ethnicity as “state-sponsored” and performed according to a “script” during periods of ethnic conflict, I argue that the Liberation Tigers of Tamil Eelam (LTTE) created a script for Tamil identity that positioned Tamil women as emblematic of the nation. The LTTE did so by portraying Tamil women as victims in need of protection and by arguing that their liberation from patriarchal traditions represented the liberation of Tamils as a whole. I then examine how the LTTE was both an agent and a product of the condition of violence during this period, thereby facilitating both agency for and oppression of women. Finally, I discuss how Tamil women sought their own agency by at times following scripts for the performance of their identities and at times deviating from those scripts.
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Pederson, Eric. "Mirror-image discrimination among nonliterate, monoliterate, and biliterate Tamil subjects." Written Language and Literacy 6, no. 1 (December 3, 2002): 71–91. http://dx.doi.org/10.1075/wll.6.1.04ped.

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In Danziger & Pederson 1998, the suggestion was made that the discrimination of left/right mirror images from non-reflected images in a part/whole judgment task correlates not just with degree of literacy, but also with the nature of the script in wich the subject is literate. This follow-up study directly addresses this hypothesis by comparing acceptance/rejection responses and the reaction time of these responses among Tamil speakers who were coded for type of literacy (nonliterate, literate only with Tamil script, literate with Tamil and Roman script). There was a reliable difference between the monoliterate and biliterate speakers in that biliterate Tamils were far more likely to reject mirror images than monoliterate Tamils. The critical difference between the monoliterates and biliterates is taken to be whether a graphemic distinction (i.e. a distinction which is symbolically meaningful) between left/right reflections has been acquired.
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T, Prabakaran, and Sundaram L. "Guidance to Transcription in Tamil Language with Reference to Tamil Grammar Texts." International Research Journal of Tamil 4, S-8 (June 27, 2022): 28–47. http://dx.doi.org/10.34256/irjt22s85.

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The mix of foreign words in Tamil is inevitable due to the mix of foreign languages, translation, globalization, scientific and technological development, new discoveries, and passion for English. Other scripts that are not in Tamil are used in some of the words, so they are mixed. When writing Northern language words, including Sanskrit in Classical Tamil, we used the phonetic letters s, j, sh, h, x, and sri, which are called Grantha letters. Later, when writing English and other words in Tamil, it became necessary to write with such letters for sounds that are not in Tamil. Apart from this, Tamil grammar texts have from time to time stated some instructions for transcribing in Tamil script in accordance with the Tamil phonetic system. This article seeks to summarise the grammar of Tamil grammar texts from Tolkappiyam to Tamilkaappu Iyam, published in 2005, and how their grammar is referred to as Northern Translation/Other Translation and their current use.
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K, Kiruthika, and Ankayarkanni S. "Iyothee Thass Alternate Script." Indian Journal of Tamil 1, no. 1 (February 25, 2020): 22–26. http://dx.doi.org/10.34256/ijot2012.

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Veera Maa Munivar is known as the pioneer in the reformation of Tamil script. Next to him, comes Periyar. But, Pandit Iyothee Thass, the precursor of Periyar is not brought into the picture. Iyothee Thass, a versatile scholar is not known to many because of his religious based ideologies. His research was based on the ideologies of Buddhism. All his activities were carried out following Buddha’s teachings. He whipped the Brahmins for suppressing the lay people, untouchables, Paraiyar, Pulaiyar on the basis of casteism and pointed out their mistake in treating them as slaves and pulling down their economy. He opines that Buddists and Jains protected and nurtured Tamil language. He brings out his devotion towards Buddha by interpolating Buddhas’s teachings even in the script changing process of ‘mai’ and ‘mei’.
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E.K, Ramasamy. "Script Reform - Periyar's Approach." International Research Journal of Tamil 4, S-5 (August 25, 2022): 132–36. http://dx.doi.org/10.34256/irjt22s520.

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Periyar emphasized the reformation of writing considering the print characters, compositor, waste of time, cost of money, and difficulty in learning. To publish his thoughts, speeches and writings in magazines and to document them in books, he needed a suitable script reformation. With experience, introspection and a history of writing script reform, his efforts came to fruition. Its echoes are still heard. Whatever reform Periyar was inclined to do, he would think it over for several days and then plan it. He does not care about success or failure. He intended to simplify Tamil language learning, teaching, typing, and printing. So he tried to bring harmony to the Tamil script. At first he demonstrated and followed by doing it himself. He then held a research seminar with an expert committee and recommended the findings to the government. It was fulfilled during Periyar's centenary celebration. Periyar's approach was successful. The article will explain this fact with historical background.
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Sproat, Richard. "Brahmi-derived scripts, script layout, and segmental awareness." Written Language and Literacy 9, no. 1 (July 20, 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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Wali, Aamir, Richard Sproat, Prakash Padakannaya, and Bhuvaneshwari B. "Model for phonemic awareness in readers of Indian script." Written Language and Literacy 12, no. 2 (December 15, 2009): 161–69. http://dx.doi.org/10.1075/wll.12.2.02wal.

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Previous studies have shown that segmental awareness tasks are usually influenced by the script. In this paper, we extend these studies further to propose a more concrete, script-centric metric for evaluating phonemic awareness in readers of Indian scripts. We propose that the ease or difficulty with which syllabic and phonemic segmental tasks are performed is directly proportional to the editing operations involved in applying the same task on the graphic form of the stimulus. We also present a computational model that calculates visual edit distances and uses these to determine if the phonemic task on any given word is easy or difficult. Finally, we tested the predictions of our model using Tamil data.
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S, Dhivya, and Usha Devi G. "TAMIZHİ: Historical Tamil-Brahmi Script Recognition Using CNN and MobileNet." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 3 (July 3, 2021): 1–26. http://dx.doi.org/10.1145/3402891.

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Computational epigraphy is the study of an ancient script where the computer science and mathematical model is relatively built for epigraphy. The Tamil-Brahmi inscriptions are the most ancient of the extant written of the Tamil. The inscriptions furnish valuable information on many aspects of life in the ancient Tamil country from a period anterior to the literary age of Sangam. The recognition of the script and systematic analysis of the script is required. The recognition of this script is complex, containing various curves for a single character and the style of writing overlap with curves and lines. Generating corpus of the script is necessary, since it is the initial step for computational epigraphy. The archaeological department has supported the raw data that helped to develop a corpus of Tamizhi. In this article, we have implemented a convolution neural network in various ways, i.e., (i) Training the CNN model from scratch a Softmax classifier in a sequential model (ii) using MobileNet: Transfer learning paradigm from a pre-trained model on a Tamizhi dataset (iii) Building Model with CNN and SVM (iv) SVM for evaluation of best accuracy to recognize handwritten Brahmi characters. To train the CNN Model an extensive TAMIZHİ handwritten Brahmi Dataset of 1lakh and 90,000 isolated samples for the character has been created and deployed. The designed dataset consists of 9 vowels and 18 consonants and 209 class so researchers can use machine learning. MobileNet outperformed among all the models implemented with the accuracy of 68.3%, whereas other algorithm ranges from 58% to 67% with respect to the Tamizhi dataset. MobileNet model is trained and tested for the dataset of vowels (8 class), consonants (18 class), and consonants vowels (26 class) with the accuracy of 98.1%, 97.7%, 97.5%, respectively.
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Munivel, Monisha, and V. S. Felix Enigo. "Optical Character Recognition for Printed Tamizhi Documents using Deep Neural Networks." DESIDOC Journal of Library & Information Technology 42, no. 4 (July 19, 2022): 227–33. http://dx.doi.org/10.14429/djlit.42.4.17742.

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Tamizhi (Tamil-Brahmi) script is one of the oldest scripts in India from which most of the modern Indian scripts are evolved. The ancient historical documents are generally preserved as digitised texts using Optical Character Recognition (OCR) technique. But the development of OCR for Tamizhi documents is highly challenging as many characters have similar shapes and structures with very small variations. In specific, for Tamizhi script it is very difficult to build an OCR as many characters are combined characters. This can be a single character formed by a single vowel/consonant or compound characters formed by combining vowels and consonants. This paper deals with the development of Tamizhi OCR for printed Tamizhi documents which is anticipated to perform efficiently irrespective of poor quality, noises and various input formats of Tamizhi documents. This is a preliminary study towards developing an OCR for handwritten Tamizhi inscription images that recognises text captured from onsite inscriptions. The developed Tamizhi OCR for printed text can produce an accuracy of about 91.12 per cent.
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Karthigaiselvi, M., and T. Kathirvalavakumar. "Recognition of Words in Tamil Script Using Neural Network." International Journal of Engineering Research and Applications 07, no. 03 (April 2017): 62–70. http://dx.doi.org/10.9790/9622-0703066270.

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Dissertations / Theses on the topic "Tamil script"

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Kunwar, Rituraj. "Incremental / Online Learning and its Application to Handwritten Character Recognition." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/366964.

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In real world scenarios where we use machine learning algorithms, we often have to deal with cases where input data changes its nature with time. In order to maintain the accuracy of the learning algorithm, we frequently have to retrain our learning system, thereby making the system inconvenient and unreliable. This problem can be solved by using learning algorithms which can learn continuously with time (incremental/ online learning). Another common problem of real-world learning scenarios that we often have to deal with is acquiring large amounts of data which is expensive and time consuming. Semi-supervised learning is the machine learning paradigm concerned with utilizing unlabeled data to improve the precision of classifier or regressor. Unlabeled data is a powerful and easily available resource and it should be utilized to build an accurate learning system. It has often been observed that there is a vast amount of redundancy in any huge, real-time database and it is not advisable to process every redundant sample to gain the same (already acquired) knowledge. Active learning is the learning setting which can handle this issue. Therefore in this research we propose an online semi-supervised learning framework which can learn actively. We have proposed an "online semi-supervised Random Naive Bayes (RNB)" classifier and as the name implies it can learn in an online manner and make use of both labeled and unlabeled data to learn. In order to boost accuracy we improved the network structure of NB (using Bayes net) to propose an Augmented Naive Bayes (ANB) classifier and achieved a substantial jump in accuracy. In order to reduce the processing of redundant data and achieve faster convergence of learning, we proposed to conduct incremental semi-supervised learning in active manner. We applied the proposed methods on the "Tamil script handwritten character recognition" problem and have obtained favorable results. Experimental results prove that our proposed online classifiers does as well as and sometimes better than its batch learning counterpart. And active learning helps to achieve learning convergence with much less number of samples.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
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Books on the topic "Tamil script"

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1950-, Murugan V., John Samuel G. 1948-, and Institute of Asian Studies (Madras, India), eds. Tolkāppiyam in English: Translation, with the Tamil text, transliteration in the roman script, introduction, glossary, and illustrations. Chennai: Institute of Asian Studies, 2001.

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Kantaiyā, Ā. Modification of Tamil scripts. Nugegoda: Open University, 1986.

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Lawrence, S. Jean. Tamil scripts, work book: Introductory course. Chennai: International Institute of Tamil Studies, 1996.

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Abad, Alicia Mira. Fancies + fantasies = scraps. Udumalpet: Ennes Publications, 1998.

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South Indian scripts in Sanskrit manuscripts and prints: Grantha Tamil-Malayalam-Telugu-Kannada-Nandinagari. Wiesbaden: Harrassowitz, 2001.

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Build, Mount. Thirikkural: A Modern Reconstruction in Tamil Script. DrumWork, 2020.

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Build, Mount. Beat Book: A Conception in Tamil Script from Ancient Artifacts. DrumWork, 2019.

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Iravatham, Mahadevan, ed. Airāvati: Felicitation volume in honour of Iravatham Mahadevan. Chennai: Varalaaru.com, 2008.

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Gmbh, Babadada. BABADADA black-and-white, Papiamento - Tamil , diccionario visual - visual dictionary: Papiamento - ... script), visual dictionary. Babadada, 2020.

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Ilamurugu, Durai. Archelogical Sites, Urbanisation and Keealady in Tamil Nadu: With a Short Note on Tamil Brahmi Scripts. Independently Published, 2019.

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Book chapters on the topic "Tamil script"

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Kathirvalavakumar, Thangairulappan, and M. Karthigai Selvi. "Efficient Touching Text Line Segmentation in Tamil Script Using Horizontal Projection." In Mining Intelligence and Knowledge Exploration, 279–88. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_29.

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Anilkumar, Chunduru, Aravind Karrothu, and G. Aishwaryalakshmi. "Framework for Statistical Machine Recognition and Translation of Ancient Tamil Script." In Smart Intelligent Computing and Applications, Volume 2, 513–22. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9705-0_50.

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Subashini, P., M. Krishnaveni, and N. Sridevi. "Period Prediction System for Tamil Epigraphical Scripts Based on Support Vector Machine." In Information Systems for Indian Languages, 23–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_4.

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Swethalakshmi, H., C. Chandra Sekhar, and V. Srinivasa Chakravarthy. "Spatiostructural Features for Recognition of Online Handwritten Characters in Devanagari and Tamil Scripts." In Lecture Notes in Computer Science, 230–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_24.

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Manavalan, Amutha. "Auteurs and Transitions in Cinema." In Handbook of Research on Social and Cultural Dynamics in Indian Cinema, 274–91. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3511-0.ch023.

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The chapter focuses on the transition in Tamil cinema and its subtle yet eloquent approach given by the auteurs in the recent past (from 2000-2018). An auteur film involves subjective and personalised filmmaking, rather than the mechanical transposition of a script onto film. An auteur film is about the filmmaking practices engaged in filming a script. Despite having two different ideologies in this industry, each auteur has managed to achieve and prove a point to the audience and the critiques. These directors were exposed to world cinemas at film institutes and motivated to make similar films in their mother tongue. Expression through cinema was the new identity each of them was seeking identical to that of the audience of Tamil cinema. Some auteurs use formulas of box office success to find it whereas some use innovative ways in cinema to find it.
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"TAMIL." In The Routledge Handbook of Scripts and Alphabets, 117–19. Routledge, 2003. http://dx.doi.org/10.4324/9780203169483-36.

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Richman, Paula. "The Ramayana Narrative Tradition as a Resource for Performance." In Performing the Ramayana Tradition, 3–28. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197552506.003.0001.

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This chapter examines case studies of Ramayana performances from different Indian regions and their relationships to diverse Ramayana texts, and provides a map of the volume. “Orientations and Beginnings” sets out an overview of intersections between Ramayana Studies and Performance Studies. “The Politics of Caste” deals with a Hindi poem and play scripts about the beheading of Shambuk, a Shudra character in the Ramayana tradition, linking them to Dalit demands for equality in 20th- and 21st-century India. “Interrogating the Anti-Hero” examines performances centered on Ravana as anti-hero or as dissident artist. “Performing Gender” analyzes two South Indian performances: a modern Tamil play critiquing patriarchal constructions of the Ramayana, and Nangyarkuttu, a pre-modern solo dance form in Kerala, whose reconstruction is infused with a woman’s contemporary sensibility. “Conversations and Arguments” highlights debates about Ramayana productions in Kutiyattam, Sattriya, and Talamaddale. “Beyond Enactment” examines effects of Ramayana performances on everyday life and society.
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"Certain Times in Uncertain Places: A Study on Scribal Colophons of Manuscripts Written in Tamil and Tamilian Grantha Scripts." In Tracing Manuscripts in Time and Space through Paratexts, 59–130. De Gruyter, 2016. http://dx.doi.org/10.1515/9783110479010-004.

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Ciotti, Giovanni. "Scribe, Owner, or Both? Some Ambiguities in the Interpretations of Personal Names in Colophons from Tamil Nadu." In The Syntax of Colophons, 149–70. De Gruyter, 2022. http://dx.doi.org/10.1515/9783110795271-005.

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Franceschini, Marco. "A Modular Framework for the Analysis of the Dates Found in Manuscripts Written in the Tamil and Tamilian Grantha Scripts." In The Syntax of Colophons, 171–208. De Gruyter, 2022. http://dx.doi.org/10.1515/9783110795271-006.

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Conference papers on the topic "Tamil script"

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Kunwar, Rituraj, and A. G. Ramakrishnan. "Online Handwriting Recognition of Tamil Script Using Fractal Geometry." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.279.

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Mahalakshmi, M., and Malathi Sharavanan. "Ancient Tamil script and recognition and translation using LabVIEW." In 2013 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2013. http://dx.doi.org/10.1109/iccsp.2013.6577211.

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Sundar, K. Ajay, and Mala John. "A high precision printed character recognition method for Tamil script." In 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2013. http://dx.doi.org/10.1109/iccic.2013.6724285.

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Urala, K. Bhargava, A. G. Ramakrishnan, and Sahil Mohamed. "Recognition of open vocabulary, online handwritten pages in Tamil script." In 2014 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2014. http://dx.doi.org/10.1109/spcom.2014.6984002.

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Chanda, Sukalpa, Srikanta Pal, and Umapada Pal. "Word-wise Sinhala Tamil and English script identification using Gaussian kernel SVM." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761823.

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Thangairulappan, Kathirvalavakumar, and Karthigaiselvi Mohan. "Efficient segmentation of printed tamil script into characters using projection and structure." In 2017 Fourth International Conference on Image Information Processing (ICIIP). IEEE, 2017. http://dx.doi.org/10.1109/iciip.2017.8313765.

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Suganya, T. S., and S. Murugavalli. "Feature selection for an automated ancient Tamil script classification system using machine learning techniques." In 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, 2017. http://dx.doi.org/10.1109/icammaet.2017.8186731.

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Abubacker, Nirase Fathima, and Raman Indra Gandhi. "An extended method for recognition of broken typewritten characters special reference to tamil script." In 2011 IEEE Conference on Open Systems (ICOS). IEEE, 2011. http://dx.doi.org/10.1109/icos.2011.6079265.

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Prasanth, L., V. Babu, R. Sharma, G. V. Rao, and Dinesh M. "Elastic Matching of Online Handwritten Tamil and Telugu Scripts Using Local Features." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4377071.

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Huang, Zhenyu, Yongjun Wang, Hongzuo Xu, Songlei Jian, and Zhongyang Wang. "Script event prediction based on pre-trained model with tail event enhancement." In CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3507548.3507585.

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