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Investigating optimal Machine Learning Techniques for the Detection of 4 Devanagari Languages in Roman script

October 10, 2022 2401 4 Analytics Data Science & AI Community AI Inside

Investigating optimal Machine Learning Techniques for the Detection of 4 Devanagari Languages in Roman script

Due to diversity in languages in India and lack of support for Indic languages in digital and physical keyboards, a common phenomenon, especially in online modes of communication, is the utilization of the roman script for Indic languages. This form of transliteration is quite common. As such, identification of the root language which is being transliterated can have many potential uses in translation, messaging, and search systems. It is therefore necessary to develop a rapid, accurate, and light model for the purpose of this detection. This paper presents an exploration of various standard textual classification techniques to achieve such a model. The paper is focused on 4 Devanagari languages: Hindi, Gujarati, Marathi and Sindhi. The machine learning models tested were a Multinomial Naive Bayes algorithm, along with a Recurrent Neural Network and a Convolutional Neural Network. The highest accuracy achieved was 97.3%.

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Aditya Mehta , Reverie Language Technologies. Reverie mentors Ashis Samal and Bhupen Chauhan. 


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