Handwritten digit recognition of Indian scripts: A cascade of distances approach

The recognition of handwritten digits remains a difficult problem, particularly in some scripts where there exists a large variation of style across writers. This large variability is an interesting challenge for algorithms in image processing and pattern recognition. Thanks to the accelerating progress and availability of low cost computers, high speed networks, and software for high performance distributed computing, it is possible to use computational expensive technique on large databases. In this paper, we propose to investigate the impact on the accuracy of different parameters and pre-processing methods of a distance based on image distortion models. A key challenge is to reduce the processing time of the nearest neighbor classification by considering rejection rules and adaptive distances. We propose to evaluate the performance of single character recognition on three databases of Indian handwritten digits, each database corresponds to a popular Indian script: Bangla, Devnagari, and Oriya. We show that the extraction of features related to four directions allows a significant improvement of the accuracy. The proposed approach takes advantages of GPU and high performance clusters, providing state-of-the-art performances.