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‘Agaram’ – Web Application of Tamil Characters Using Convolutional Neural Networks and Machine Learning

  • J. RamyaEmail author
  • Goutham Kumar Raj Kumar
  • Chrisvin Jem Peniel
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

This paper aims to explore the scope of these neural networks and apply them to try and recognize handwritten data which consists of Tamil characters written by various people and convert it to a computerized text document. Through our system we will be targeting one of the gaps in the currently available technology, which is to properly identify and distinguish the characters in ancient manuscripts.

Machine Learning will be used to train the system to recognize the fed data and convolutional neural networks will be used to make decisions on its own and by doing that, it improves the accuracy of the prediction. The application of this system extends to various fields such as history, archaeology, paleography, engineering etc.

Keywords

Convolutional neural networks Machine learning Artificial intelligence 

References

  1. 1.
    Wahi, A., Sundaramurthy, S., Poovizhi, P.: Handwritten Tamil character recognition using zernike moments and legendre polynomial. In: Suresh, L., Dash, S., Panigrahi, B. (eds.) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol. 325. Springer, New Delhi (2015)Google Scholar
  2. 2.
    Selvakumar, P., Ganesh, S.H.: Tamil character recognition using canny edge detection algorithm. In: 2017 World Congress on Computing and Communication Technologies (WCCCT) (2017)Google Scholar
  3. 3.
    Kannan, R.J., Subramanian, S.: An adaptive approach of Tamil character recognition using deep learning with big data-a survey. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds.) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol. 337. Springer, Cham (2015)Google Scholar
  4. 4.
    Aparna, K.G., Ramakrishnan, A.G.: A complete Tamil optical character recognition system. In: Lopresti, D., Hu, J., Kashi, R. (eds.) Document Analysis Systems V. DAS 2002. Lecture Notes in Computer Science, vol. 2423. Springer, Berlin (2002)Google Scholar
  5. 5.
    Perwej, Y., Chaturvedi, A.: Neural networks for handwritten English alphabet recognition. Int. J. Comput. Appl. (0975–8887) 20(7) (2011)CrossRefGoogle Scholar
  6. 6.
    Al-Omari, S.A.K., Sumari, P., Al-Taweel, S.A., Husain, A.J.A.: Digital recognition using neural network. J. Comput. Sci. 5(6), 427–434 (2009). ISSN 1549-3636 © 2009 Science PublicationsCrossRefGoogle Scholar
  7. 7.
    Ramya, J., Parvathavarthini, B.: Feed forward back propagation neural network based character recognition system for Tamil palm leaf manuscripts. J. Comput. Sci. 10(4), 660–670 (2014). ISSN 1549-3636CrossRefGoogle Scholar
  8. 8.
    Ramya, J., Parvathavarthini, B.: Segmentation of Tamil palm leaf manuscripts images. Eur. J. Sci. Res. 91(4), 587–603 (2012). (ISSN 1450-216X)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • J. Ramya
    • 1
    Email author
  • Goutham Kumar Raj Kumar
    • 1
  • Chrisvin Jem Peniel
    • 1
  1. 1.Department of Computer Science and EngineeringSt. Joseph’s College of EngineeringChennaiIndia

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