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Journal articles on the topic 'Transliteration into Tamil'

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1

Herath, H. M. Anuja Dilrukshi, and T. G. Deshan K. Sumanathilaka. "TAMZHI: Shorthand Romanized Tamil to Tamil Reverse Transliteration Using Novel Hybrid Approach." International Journal on Advances in ICT for Emerging Regions (ICTer) 17, no. 1 (2024): 1–7. http://dx.doi.org/10.4038/icter.v17i1.7271.

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Transliteration from Tamil to the Roman script holds a crucial place in the realms of effective communication, educational accessibility, and the seamless integration of digital technology. However, this process encounters a significant challenge due to the disparity in the number of vowels between the Tamil script, which encompasses a rich set of 12 vowels, and the Roman script, which is limited to just 5. This incongruity poses a substantial impediment when attempting ad-hoc transliteration of Tamil into the Roman script, especially when vowels are omitted. This paper aims to make a signific
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2

M, Mercy Evangeline, and Shyamala K. "VERB IDENTIFICATION USING MORPHOPHONEMIC RULES IN TAMIL LANGUAGE." ICTACT Journal on Soft Computing 11, no. 1 (2020): 2237–43. https://doi.org/10.21917/ijsc.2020.0320.

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Part of Speech (POS) tagging is a general process of classifying words into their parts, labeling them into different categories like Nouns, Verbs, Adjective, and Adverbs and so on. These different categories of words can be defined in a tagset. The tagset can be used for automatically assigning a word with a tag. Noun forms the first category of words generally found in any sentence and verb comes next. A verb actually describes the event occurrence or an action.in Tamil language, a verb form gets inflected by suffixes based on person, count, tense and voice. These suffixes are identified by
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3

M., Prakash, Ojha Apoorva, and Raman Priyanshu. "Tamil Character Recognition, Translation and Transliteration System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1762–67. https://doi.org/10.35940/ijeat.D7633.049420.

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Optical Character Recognition (OCR) is the machine conversion of handwritten or typed data into machine encoded scripts. English, is the informal link to all the regional languages in India and is used to publish reports, papers, magazines and records. Monolingual systems are incapable of doing so, thus increasing the need of bilingual frameworks. We have created a character recognition system that converts the user’s input in the Tamil language to English. Additionally, we can also perform transliteration of Tamil to English and vice versa. The bilingual OCR system, “MOZHI VALLAAN
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4

Mahadevan, Iravatham. "Persian-Tamil inscription from āmbūr Fort." Bulletin of the School of Oriental and African Studies 51, no. 3 (1988): 540–41. http://dx.doi.org/10.1017/s0041977x00116544.

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I read with interest the brief report on the Persian–Tamil inscription on a stone tablet recording the reconstruction of the Āmbūr Fort in Tamilnadu, and now preserved in the Victoria and Albert Museum (BSOAS, XLIX, 3, 1986, 553–7). As a Tamil epigraphist, I was particularly interested in the Tamil text edited by J. R. Marr (ibid., 553, n. 3). Unfortunately, he has misread two words (each occurring twice), made wrong segmentations of two other words and omitted the last word in the text. These happen to be crucial words and consequently his translation has suffered. The Tamil characters of the
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5

Brindha, S., and S. Bhuvaneswari. "Repossession and Recognition System: Transliteration of Antique Tamil Brahmi Typescript." Current Science 120, no. 4 (2021): 654. http://dx.doi.org/10.18520/cs/v120/i4/654-665.

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6

Markandan, Rubavathanan. "Green’s Terminological principles and methods: an analysis on the pioneering work of Scientific Tamil." International Research Journal of Tamil 3, no. 4 (2021): 206–17. http://dx.doi.org/10.34256/irjt21425.

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This research paper attempts to provide a details descriptive analysis on the terminological work done by Samuel Fisk Green, an American Medical Missionary as part of the Scientific Tamil initiatives in the nineteenth century in Jaffna, Sri Lanka. Green being a foreign or more accurately an alien to Tamil language started to teach western medicine in Tamil at his own Medical College at Manipay and translated medical science related books from European languages into Tamil to enhance medium of instruction and disseminate healthcare information within community. During the translation process he
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7

S, Thivaharan, Gayathri C, and Vijay Raj Kumar M. "Thirukkural Alagiduthal and Kural Venba Validation using Python." Journal of Trends in Computer Science and Smart Technology 5, no. 1 (2023): 51–66. http://dx.doi.org/10.36548/jtcsst.2023.1.004.

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Thirukkural, one of the prominent Tamil Literatures, written 2000 years ago, has great importance till date as it emphasizes everyday virtues of the individuals. As per Tamil Literature, it belongs to the class of ‘Kural Venba’ which is a subdivision of ‘Venba’ and is regarded as one of the poetic styles of Tamil Language. This research primarily aims on the validation of entered Thirukkural by checking the syntactical correctness of ‘Kural Venba’. It uses ‘Transliteration’ methodology to convert the user given thirukkural into an intermediate form. Then, it processes the intermediate form to
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8

அ., ஞா. உமாமகேஸ்வரி /. A. G. Umamaheswari. "இசைத்தமிழ் நோக்கில் திருவாசகம் / Thiruvasagam in the Perspective of Tamil Music". IJTLLS 7, SPL 2 (2025): 81–92. https://doi.org/10.5281/zenodo.15549726.

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<strong>&nbsp;</strong><em>Tamil literature holds an eternal legacy, with remarkable works like the Sangam literature, devotional hymns, and classical epics that enrich its tradition. Among these, devotional musical literature occupies a significant place. One of its finest examples is &ldquo;Thiruvasagam&rdquo; by Manickavasakar, known as the eighth Thirumurai. This devotional treatise stands out for its beautiful devotional language and profound spiritual meaning. While Sangam literature often reflects worldly experiences, Saiva literature like &ldquo;Thiruvasagam&rdquo; leads the soul towar
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9

Das, Sonia N. "Transliterating Cities: The Interdiscursive Ethnohistory of a Tamil Francophonie." Signs and Society 8, no. 1 (2020): 125–54. http://dx.doi.org/10.1086/706249.

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10

Dorairajan, Uthra, and Manikandan Sambasivam. "An Overview on Steps to Accelerate Science Communication in Tamil." International Research Journal of Tamil 4, no. 2 (2022): 45–53. http://dx.doi.org/10.34256/irjt2226.

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Our country’s Science, Technology, Innovation Policy (STIP 2020) was released last year. Generally, there exists a gap between science and technology and the general public. It is impertinent to bridge this gap between scientific domain experts, technology aficionados and common (wo)man. STIP 2020 aims to maximize the number of people that participate in and contribute to the scientific discussions, missions and processes in the country. Current science and technology innovations, opportunities, and efforts need to reach every citizen through every spoken and written language of our country. T
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11

Thomas, Merin, Dr Latha C A, and Antony Puthussery. "Identification of language in a cross linguistic environment." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (2020): 544. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp544-548.

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&lt;p class="normal"&gt;World has become very small due to software internationationalism. Applications of machine translations are increasing day by day. Using multiple languages in the social media text is an developing trend. .Availability of fonts in the native language enhanced the usage of native text in internet communications. Usage of transliterations of language has become quite common. In Indian scenario current generations are familiar to talk in native language but not to read and write in the native language, hence they started using English representation of native language in t
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12

Merin, Thomas, C. A. Latha, and Puthussery Antony. "Identification of language in a cross linguistic environment." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 1 (2020): 544–48. https://doi.org/10.11591/ijeecs.v18.i1.pp544-548.

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World has become very small due to software internationationalism. Applications of machine translations are increasing day by day. Using multiple languages in the social media text is a developing trend. Availability of fonts in the native language enhanced the usage of native text in internet communications. Usage of transliterations of language has become quite common. In Indian scenario current generations are familiar to talk in native language but not to read and write in the native language, hence they started using English representation of native language in textual messages. This pape
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13

"Tamil Character Recognition, Translation and Transliteration System." International Journal of Engineering and Advanced Technology 9, no. 4 (2020): 1762–67. http://dx.doi.org/10.35940/ijeat.d7633.049420.

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Abstract:
Optical Character Recognition (OCR) is the machine conversion of handwritten or typed data into machine encoded scripts. English, is the informal link to all the regional languages in India and is used to publish reports, papers, magazines and records. Monolingual systems are incapable of doing so, thus increasing the need of bilingual frameworks. We have created a character recognition system that converts the user’s input in the Tamil language to English. Additionally, we can also perform transliteration of Tamil to English and vice versa. The bilingual OCR system, “MOZHI VALLAAN,” which has
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14

Anbukkarasi, S., D. Elangovan, Jayalakshmi Periyasamy, et al. "Phonetic based Forward Online Transliteration Tool from English to Tamil Language." International Journal of Reliability, Quality and Safety Engineering, February 17, 2023. http://dx.doi.org/10.1142/s021853932350002x.

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15

Das, Sudhansu Bala, Divyajyoti Panda, Tapas Kumar Mishra, Bidyut Kr Patra, and Asif Ekbal. "Multilingual Neural Machine Translation for Indic to Indic Languages." ACM Transactions on Asian and Low-Resource Language Information Processing, March 12, 2024. http://dx.doi.org/10.1145/3652026.

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The method of translation from one language to another without human intervention is known as Machine Translation (MT). Multilingual neural machine translation (MNMT) is a technique for MT that builds a single model for multiple languages. It is preferred over other approaches since it decreases training time and improves translation in low-resource contexts, i.e. for languages that have insufficient corpus. However, good-quality MT models are yet to be built for many scenarios such as for Indic-to-Indic Languages (IL-IL). Hence, this paper is an attempt to address and develop the baseline mod
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