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Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 2796863, 17 pages
Research Article

A Study of Moment Based Features on Handwritten Digit Recognition

Department of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, India

Received 3 November 2015; Revised 16 January 2016; Accepted 27 January 2016

Academic Editor: Miin-Shen Yang

Copyright © 2016 Pawan Kumar Singh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample. Finally, the technique is evaluated on CMATER and MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP) classifier outperforms the others. Satisfactory recognition accuracies are attained for all the five mentioned scripts.