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Reseach Article

Self-Learning by Word Localization from Images

by Saranya Manoharan, Muthu Kumar B
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 26
Year of Publication: 2014
Authors: Saranya Manoharan, Muthu Kumar B
10.5120/16958-7035

Saranya Manoharan, Muthu Kumar B . Self-Learning by Word Localization from Images. International Journal of Computer Applications. 95, 26 ( June 2014), 13-16. DOI=10.5120/16958-7035

@article{ 10.5120/16958-7035,
author = { Saranya Manoharan, Muthu Kumar B },
title = { Self-Learning by Word Localization from Images },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 26 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number26/16958-7035/ },
doi = { 10.5120/16958-7035 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:28.324178+05:30
%A Saranya Manoharan
%A Muthu Kumar B
%T Self-Learning by Word Localization from Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 26
%P 13-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Intelligence is an interdisciplinary research area which aims at making the machines more human. Extensive research is going on to teach them to perform the tasks. Deep learning is a collection of algorithms in Machine Learning. In this paper we implement deep learning for learning and gaining the knowledge of the text from real time images. An algorithm namely word localization is proposed to able to make the machine to understand the words extracted from the images. In comparison with traditional Optical character recognition (OCR) it has many advantages over it which is been analyzed.

References
  1. G. E. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, pp. 1527–1554, 2006.
  2. Y. Freund and D. Haussler, "Unsupervised learning of distributions on binary vectors using two layer networks," Technical Report UCSC-CRL-94-25, University of California, Santa Cruz, 1994.
  3. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layer-wise training of deep networks," in Advances in Neural Information Processing Systems 19 (NIPS'06), (B. Scholkopf, J. Platt, and T. Hoffman, eds. ), pp. 153–160, MIT Press, 2007.
  4. M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, "Efficient learning of sparse representations with an energy-based model," in Advances in Neural Information Processing Systems 19 (NIPS'06), (B. Sch¨olkopf, J. Platt, and T. Hoffman, eds. ), pp. 1137–1144, MIT Press, 2007.
  5. M. Belkin, I. Matveeva, and P. Niyogi, Regularization and semi-supervised learning on large graphs," in Proceedings of the 17th International conference on Computational Learning Theory (COLT'04), (J. Shawe- Taylor and Y. Singer, eds. ), pp. 624–638, Springer, 2004.
  6. O. Delalleau, Y. Bengio, and N. L. Roux, "Efficient non- parametric function induction in semi-supervised learning," in Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, (R. G. Cowell and Z. Ghahramani, eds. ), pp. 96–103, Society for Artificial Intelligence and Statistics, January 2005.
  7. Pardis Noorzad, "Feature Learning from Deep Networks for Image classification," in Computer Vision Seminar.
  8. Ming Zhao, Shutao Li and James Kwok, "Text detection in images using sparse representation with discriminative dictionaries," Elsevier on Image and Vision Computing, 28, 2010.
  9. F. Chen, H. Yu and R. Hu, "Shape sparse representation for joint object classification and segmentation", IEEE Trans. image processing, 22(3):992-1004, 2013.
  10. D. Ciresan, J. Schmidhuber. "Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification", 1 Sep 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Deep Learning Unsupervised Learning Supervised Learning Robot Vision Robot Grasping Machine Vision Word Localization Knowledge transfer of text