A Brief Review of Classifiers used in OCR Applications

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-34 Number-2
Year of Publication : 2016
Authors : Satish Kumar
  10.14445/22312803/IJCTT-V34P114

MLA

Satish Kumar "A Brief Review of Classifiers used in OCR Applications". International Journal of Computer Trends and Technology (IJCTT) V34(2):80-88, April 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The performance of a recognition system depends upon the classifiers used for classification purpose. Powerful is the discrimination ability of a classifier, better is its recognition performance. The generalization ability of a classifier is measured on the basis of its performance in classifying the test patterns. There are various factors which affect generalization. Moreover, the feature extraction method(s) used for training a classifier also affects the performance of a classifier. In this paper, a brief theoretical review of various classifiers is made. The various characters of each are covered. The classifiers covered are Bayes, Parzen, probabilistic, polynomial, discriminant, radial basis networks, multi layer perceptron(MLP), k-NN, SVM and SOM.

References
[1] O. D. Trier, A. K. Jain and T. Taxt, ―Feature Extraction Method for Character Recognition – a Survey, Pattern Recognition, Vol. 29, No. 4, pp. 641-662(1996).
[2] S. Haykin, ―Neural Networks A Comprehensive Foundation, Second Edition, and Pearson Education, Asia.
[3] A. K. Jain and R. P. W. Duin and J. Mao, ―Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4-37(2000).
[4] R. C. Gonzalez and R. E. Woods, ―Digital Image Processing, 2nd Ed., Pearson Education.
[5] T. Kawatani, ―Handprinted Numeral Recognition with the Learning Quadratic Discriminant Function, Proceedings of the International Conference on Document Analysis and Recognition, pp. 14-17(1993).
[6] S. J. Raudys and A. K. Jain, ―Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13, No. 3, pp. 252-264(1991).
[7] Y. Hamamoto, S. Suchimura and S. Tomita, ― On the Behavior of Artificial Neural Network Classifiers in High- Dimensional Space, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 5, pp. 571-574(1996).
[8] U. Kressel and J. Schürmann, ―Pattern Classification Techniques Based on Function Approximation, Handbook of Character Recognition and Document Analysis, World Scientific , pp. 49-78(1997).
[9] A. K. Jain, J. Mao and K. Mohiuddin, ―Artificial Neural Networks: A Tutorial, IEEE Computer Special Issue on Neural Computing, pp. 31-43(1996).
[10] F. Ancona, A M. Colla, S. Rovetta and R. Zunino, ―Implementing Probabilistic Neural Networks, Neural Computing & Applications, Vol. 5, pp. 152-159(1997).
[11] N. K. Bose and P. Liang, ―Neural Network Fundamentals with Graphs, Algorithms and Applications, Tata McGraw-Hill, New Delhi.
[12] B. Yagnanarayan, ―Artificial Neural Networks, Prentice Hall India, New Delhi, (2001).
[13] A. K. Jain, J. Mao and K. Mohiuddin, ―Artificial Neural Networks: A Tutorial, IEEE Computer Special Issue on Neural Computing, pp. 31-43(1996).
[14] U. Kressel and J. Schürmann, ―Pattern Classification Techniques Based on Function Approximation, Handbook of Character Recognition and Document Analysis, World Scientific , pp. 49-78(1997).
[15] T. M. Cover and P.E. Hart, ―Nearest Neighbor Pattern Classification, IEEE Transactions on Information Theory, Vol. 13 , pp. 212-217(1967).
[16] M. Riedmiller and H. Braun, ―A Direct Adaptive Method for Faster Back-propagation Learning: The RPROP Algorithm, Proceedings of the IEEE International Conference on Neural Networks, Vol. 1, pp. 586-591 (1993).\
[17] S. J. Smith, M. O. Bourgoin, K. Sims and H.L. Voorhees, ―Handwritten Character Classification using Nearest Neighbor in Large Database, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, No. 9, 915-919(1994).
[18] Zs. M. Kovics and R. Guerrieri, ―Massively-Parallel Handwritten Character Recognition Based on the Distance Transform, Pattern Recognition, Vol. 28, No. 3, pp. 293- 301(1995).
[19] S. O. Belkasim, M. Shridhar and M. Ahmadi, ―Pattern Recognition with Moment Invariants: A Comparative Study and New Results, Pattern Recognition, Vol. 24, No. 12, pp. 1117-1138(1997).
[20] G. S. Lehal and C. Singh, ―Feature Extraction and Classification for OCR of Gurmukhi Script, Vivek, Vol. 12, pp. 2–12(1999).
[21] S. Antani and L. Agnihotri, ―Gujarati Character Recognition, Proceedings of the Fifth International Conference on Document Analysis and Recognition, Bangalore, India, pp. 418–421(1999).
[22] S. D. Connel, R.M.K. Sinha and A. K. Jain, ―Recognition of Unconstrained On- Line Devanagari Characters, Proceedings of the International Conference on Pattern Recognition, Barcelona, Spain, Vol. 2, pp. 368-371(2000).
[23] C. V. Jawahar, M.N.S.S. K. Pavan Kumar and S. S. Ravi Kiran, ―A Bilingual OCR for Hindi-Telugu Documents And Its Applications, International conference on Document Analysis and Recognition, Vol. 1, pp. 408- 412(2003).
[24] C. J. C. Burges, ―A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121–167 (1998).
[25] C.-W. Hsu and C.-J. Lin, ―A Comparison of Methods for Multi-class Support Vector Machines, IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415–425(2002).
[26] V. Vapnik, ―The Nature of Statistical Learning Theory Springer-Verlag, New Tork (1995).
[27] J.-X. Dong, A. Krzyzak and C. Y. Suen, ―An Improved Handwritten Chinese Character Recognition System using Support Vector Machine, Pattern Recogniotion Letters, Vol. 26, No. 12, pp. 1849-1856(2005)
[28] L. S. Oliveira and R. Sabourin, ―Support Vector Machines for Handwritten Numerical String Recognition, Ninth International Workshop on Frontiers in Handwriting Recognition, Kokubunji, Tokyo, Japan, pp. 39-44(2004).
[29] T. Joachims, ―Making Large-Scale SVM Learning Practical, In Advances in Kernel Methods- Support Vector Learning, B. Schölkopf, C.J.C. Burges, and A. J. Smola, Eds. Combridge, MA: MIT Press(1998).
[30] C.- L. Liu and M. Nakagawa, ―Evaluation of Prototype Learning Algorithms for Nearest- Neighbor Classifier in Application to Handwritten Character Recognition, Pattern Recognition, Vol. 34, pp. 601-615 (2001).

Keywords
Classifiers, Recognition, PNN, SOM, k-NN, SVM, MLP.