A framework for recognizing the hand written digits with multi-zone approach

In this paper, we proposed a handwritten digit recognition system which uses multiple feature extraction methods. Here we extract the size features, and we proposed multi-zoning method. It is shown that multi zoning method is sufficient to achieve high recognition rates. Several combination schemes were tested, showing good results. By using this multi-zoning method we achieved a recognition rate of 97%, the highest one on the MNIST database.

[1]  Ashraf A. Kassim,et al.  Dual classifier system for handprinted alphanumeric character recognition , 1998, Pattern Analysis and Applications.

[2]  Adavi Balakrishna,et al.  Implementation of object oriented approach for copyright protection using Hadamard transforms , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

[3]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[4]  Marc'Aurelio Ranzato,et al.  Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.

[5]  Nikos Fakotakis,et al.  Handwritten word recognition based on structural characteristics and lexical support , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[6]  R. S. Shankar,et al.  Implementation of object oriented approach to query processing for video subsequence identification , 2012, 2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS.

[7]  Ping Zhang,et al.  Reliable recognition of handwritten digits using a cascade ensemble classifier system and hybrid features , 2006 .

[8]  R. Shiva Shankar,et al.  Object oriented fuzzy filter for noise reduction of Pgm images , 2012, 2012 8th International Conference on Information Science and Digital Content Technology (ICIDT2012).

[9]  Ching Y. Suen,et al.  A trainable feature extractor for handwritten digit recognition , 2007, Pattern Recognit..

[10]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[11]  Luiz Eduardo Soares de Oliveira,et al.  Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy , 2002, IEEE Trans. Pattern Anal. Mach. Intell..