Recognition of Unconstrained Handwritten Numerals using Chaotic Neural Network

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Several neural networks have been successfully used to classify complex patterns such as handwritten numerals or words. This paper describes the discrimination of totally unconstrained handwritten numerals using the proposed chaotic neural network (CNN) to improve the recognition rate. The recognition system in the paper consists of the preprocessing stage to extract features using Kirsch mask and the classification stage to recognize numerals using the CNN. In order to evaluate the performance of the proposed network, we performed the recognition with unconstrained handwritten numeral database of Concordia university, Canada. Experimental results show that the CNN based recognizer performs higher recognition rate than other neural network-based methods reported using same database.