Kannada to English Machine Translation Using Deep Neural Network

Author(s)	Mr. Sangamesh Ramesh Yankanchi, Mr. Anil S, Mr. Abhishek Rathod
Country	India
Abstract	This paper proposes an NMT model which takes Kannada text as input and translates to the corresponding English using a sequence-to-sequence architecture with LSTM layers. The mechanism of encoder-decoder facilitates capturing and holding onto sentence context and, therefore, improves the accuracy of translation. Accuracy of 90.32\% was obtained by our approach. In the future work, Gated Recurrent Units and other transformer architectures may be extensively investigated for further improvement in Kannada and other low-resource language translation.
Keywords	Neural Machine Translation, Kannada, Sequence-to-Sequence, LSTM, Low-resource Languages, Deep Learning
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.46787
Short DOI	https://doi.org/g9pzwn

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