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Real Time Translation of Malayalam Notice Boards to English Directions

by Akshay K., Aravind Das A. M., Carral Vincent, Betty Babu, Rasmi P. S
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 26
Year of Publication: 2019
Authors: Akshay K., Aravind Das A. M., Carral Vincent, Betty Babu, Rasmi P. S
10.5120/ijca2019919079

Akshay K., Aravind Das A. M., Carral Vincent, Betty Babu, Rasmi P. S . Real Time Translation of Malayalam Notice Boards to English Directions. International Journal of Computer Applications. 178, 26 ( Jun 2019), 6-10. DOI=10.5120/ijca2019919079

@article{ 10.5120/ijca2019919079,
author = { Akshay K., Aravind Das A. M., Carral Vincent, Betty Babu, Rasmi P. S },
title = { Real Time Translation of Malayalam Notice Boards to English Directions },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2019 },
volume = { 178 },
number = { 26 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number26/30696-2019919079/ },
doi = { 10.5120/ijca2019919079 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:28.229399+05:30
%A Akshay K.
%A Aravind Das A. M.
%A Carral Vincent
%A Betty Babu
%A Rasmi P. S
%T Real Time Translation of Malayalam Notice Boards to English Directions
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 26
%P 6-10
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neural Machine Translation (NMT) is an emerging technique depicting impressive performance, better than traditional machine translation methods. It is observed that NMT models have a strong efficacy to learn language constructs, improving performance. Considered as one of the toughest Indian languages to learn and comprehend, Malayalam is extensively used in Road Signs and Notice Boards in Kerala as it increasingly becomes India’s tourism hub. In this paper, the barrier faced by the tourists is resolved by providing real-time translation to English. The results obtained show that accuracy can be improved by incorporating Deep Learning and Natural Language Processing (NLP) in translation. This paper is envisioned to not only convert notice boards but also translate Malayalam that is written and printed on all mediums.

References
  1. Bartz, Christian, Haojin Yang and Christoph Meinel. “STN-OCR: A single Neural Network for Text Detection and Text Recognition.” CoRR abs/1707.08831 (2017).
  2. Sooraj, S & K, Manjusha & Kumar, M & Kp, Soman. (2018). “Deep learning based spell checker for Malayalam language. Journal of Intelligent & Fuzzy Systems”, 34. 1427-1434. 10.3233/JIFS-169438.
  3. Remya Rajan, Remya Sivan, Remya Ravindran, K.P. Soman, “ Rule Based Machine Translation from English to Malayalam” 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, DOI: 10.1109/ACT.2009.
  4. Rongxiang Weng, Shujian Huang, Zaixiang Zheng, Xinyu Dai and Jiajun Chen, “ Neural Machine Translation with Word Predictions” State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023, China.
  5. D. Bahdanau, K. Cho, and Y. Bengio. “Neural machine translation by jointly learning to align and translate.” arXiv preprint arXiv:1409.0473, 2014.
  6. J. Su, J. Zeng, D. Xiong, Y. Liu, M. Wang and J. Xie, "A Hierarchy-to-Sequence Attentional Neural Machine Translation Model," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 3, pp. 623-632.doi: 10.1109/TASLP.2018.2789721.
  7. W. Bieniecki, S. Grabowski and W. Rozenberg, "Image Preprocessing for Improving OCR Accuracy," 2007 International Conference on Perspective Technologies and Methods in MEMS Design, Lviv-Polyana, 2007, pp. 75-80.doi: 10.1109/MEMSTECH.2007.4283429.
  8. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, “Google’s Neural Machine Translation System: Bridging the Gapbetween Human and Machine Translation”, arXiv:1609.08144v2 [cs.CL], October 2016.
  9. Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, “Achieving Human Parity on AutomaticChinese to English News Translation”, arXiv:1803.05567v2 [cs.CL], June 2018
  10. Bradski G, “The OpenCV Library”, Dr Dobb's Journal of Software Tools, 2000.
  11. Patel, Chirag & Patel, Atul & Patel, Dharmendra. (2012), “Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study”, International Journal of Computer Applications. 55. 50-56. 10.5120/8794-2784.
  12. Daniel Watson, Nasser Zalmout and Nizar Habash, “Utilizing Character and Word Embeddings for Text Normalizationwith Sequence-to-Sequence Models”, Computational Approaches to Modeling Language Lab, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.
  13. Sina Ahmadi.2017, “Attention-based encoder-decodernetworks for spelling and grammatical error correc-tion”, Master’s thesis, Paris Descartes University.
  14. B. Zhang, D. Xiong and J. Su, "Neural Machine Translation with Deep Attention," in IEEE Transactions on Pattern Analysis and Machine Intelligence.doi: 10.1109/TPAMI.2018.2876404.
  15. S. P. Singh, A. Kumar, H. Darbari, L. Singh, A. Rastogi and S. Jain, "Machine translation using deep learning: An overview," 2017 International Conference on Computer, Communications and Electronics (Comptelix), Jaipur, 2017pp.162167.doi:10.1109/COMPTELIX.2017.8003957.
Index Terms

Computer Science
Information Sciences

Keywords

Binarization Grayscale NLP unit Translation unit OpenCV

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