Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Hybrid Complex Wavelet Features for the Verification of Handwritten Numerals
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A novel free format Persian/Arabic handwritten zip code recognition system
Computers and Electrical Engineering
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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.