Handwritten Digit Recognition Using SVM Binary Classifiers and Unbalanced Decision Trees

In this work, we use SVM binary classifiers coupled with a binary classifier architecture, an unbalanced decision tree, for handwritten digit recognition. According to input variables, two classifiers were trained and tested. One using digit characteristics and the other using the whole image as input variables. Developed recently, the unbalanced decision tree architecture provides a simple structure for a multiclass classifier using binary classifiers. In this work, using the whole image as input, 100% handwritten digit recognition accuracy was obtained in the MNIST database. These are the best results published in the literature for the MNIST database.

[1]  Abhijit S. Pandya,et al.  A hybrid approach to recognize handwritten alphanumeric characters , 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.

[2]  Robert I. Damper,et al.  Classification of emotional speech using 3DEC hierarchical classifier , 2012, Speech Commun..

[3]  Miguel A. Ferrer,et al.  Combining different off-line handwritten character recognizers , 2011, 2011 15th IEEE International Conference on Intelligent Engineering Systems.

[4]  A. Ramanan,et al.  Unbalanced Decision Trees for multi-class classification , 2007, 2007 International Conference on Industrial and Information Systems.

[5]  Adel M. Alimi,et al.  A hardware implementation of neural network for the recognition of printed numerals , 2000, ICM'99. Proceedings. Eleventh International Conference on Microelectronics (IEEE Cat. No.99EX388).

[6]  Tatiana Baidyk,et al.  Improved method of handwritten digit recognition tested on MNIST database , 2004, Image Vis. Comput..

[7]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Ching Y. Suen,et al.  A class-modular feedforward neural network for handwriting recognition , 2002, Pattern Recognit..

[9]  Bernhard Schölkopf,et al.  Training Invariant Support Vector Machines , 2002, Machine Learning.

[10]  J.C.H. Poon,et al.  An enhanced approach to character recognition by Fourier descriptor , 1992, [Proceedings] Singapore ICCS/ISITA `92.

[11]  Yuk Ying Chung,et al.  Handwritten character recognition by Fourier descriptors and neural network , 1997, TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162).

[12]  Luca Maria Gambardella,et al.  Convolutional Neural Network Committees for Handwritten Character Classification , 2011, 2011 International Conference on Document Analysis and Recognition.

[13]  L. Deng,et al.  The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web] , 2012, IEEE Signal Processing Magazine.