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Deep Learning for Trilingual Character Recognition

Deep Learning for Trilingual Character Recognition

M. Yashodha, SK Niranjan, V. N. Manjunath Aradhya
Copyright: © 2019 |Volume: 8 |Issue: 1 |Pages: 7
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522566328|DOI: 10.4018/IJNCR.2019010104
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MLA

Yashodha, M., et al. "Deep Learning for Trilingual Character Recognition." IJNCR vol.8, no.1 2019: pp.52-58. http://doi.org/10.4018/IJNCR.2019010104

APA

Yashodha, M., Niranjan, S., & Aradhya, V. N. (2019). Deep Learning for Trilingual Character Recognition. International Journal of Natural Computing Research (IJNCR), 8(1), 52-58. http://doi.org/10.4018/IJNCR.2019010104

Chicago

Yashodha, M., SK Niranjan, and V. N. Manjunath Aradhya. "Deep Learning for Trilingual Character Recognition," International Journal of Natural Computing Research (IJNCR) 8, no.1: 52-58. http://doi.org/10.4018/IJNCR.2019010104

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Abstract

As India is a multilingual country, in which the national language is Hindi, regional languages still exist in each of the corresponding states. In government offices, for the purpose of communication and maintenance of files and ledgers, the languages preferred are the regional languages and Hindi. As corporate offices and private organizations also exist in the country, these bodies mainly prefer the English language with the regional language in recording documents and ledgers. So, in this regard, in India a document contains multilingual texts, and there is a need of a multilingual OCR system. In this article, a trilingual OCR system is developed using deep learning for supporting English, Hindi and Kannada languages, the regional language of the state Karnataka.

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