Unconstrained handwritten character recognition based on WEDF and Multilayer Neural Network

In this paper, we propose a new approach for unconstrained handwritten character recognition based on wavelet energy density feature (WEDF) and multilayer neural network. Unlike other method taking the wavelet coefficients directly as features, our method using the wavelet energy density features instead. The proposed approach consists of a feature extraction stage for extracting wavelet energy density features with wavelets transform, and a classification stage for classifying handwritten characters with a simple neural network. In order to verify the performance of the proposed method, experiments are carried out on handwritten numerals recognition. Experimental results indicate that the WEDF is stable and reliable in handwritten character recognition and performs better than wavelet coefficient feature, it provides high recognition rate on both training samples and testing samples.