Handwritten numeral recognition using non-redundant Stockwell transform and bio-inspired optimal zoning

Handwritten digit recognition is one of the challenging problems of character recognition because of the large variation in writing styles of individuals and the presence of similar looking shapes of different numerals. Most of the feature extraction techniques are based on statistical or topological attributes of the image in its spatial domain, barring few works attempting feature extraction in a transformed domain. Another challenge is the optimal selection of zones while extracting features from localised zones of the unknown (test) image. In most of the cases, the recognition phase, being isolated from the training phase makes it impossible to adaptively improve the feature selection using the knowledge obtained from error analysis. In this study, the authors propose a feature extraction technique, new to the character recognition problem, using non-redundant Stockwell transform. Another transformed domain feature extraction using Slantlet coefficients is proposed. They also propose to use bio-inspired and evolutionary computing-based optimisation techniques to adaptively select the optimal zone arrangement in the feature selection stage from the knowledge of classification accuracy. The proposed methods are experimentally validated on handwritten digit database of Odia language which proves to outperform any recognition accuracy reported before.