Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals

The recognition of handwritten numerals is a challenging task in pattern recognition. It can be considered as one of the benchmarks in evaluating feature extraction methods and the performance of classifiers. In this paper, we propose a new method to improve the recognition accuracy of handwritten numerals by using hybrid feature extraction and random feature selection. First, we present seven feature extraction methods. A novel multi-class divergence criterion for large scale feature analysis is proposed and a random feature selection strategy is used to congregate three new hybrid feature sets. The new congregated features are complementary as they are formed from different original feature sets extracted by different means. Experiments conducted on MNIST database show that our proposed method can increase the recognition accuracy.