Using a Haar wavelet transform, principal component analysis and neural networks for OCR in the presence of impulse noise

In this paper we propose a novel algorithm for optical character recognition in the presence of impulse noise by applying a wavelet transform, principal component analysis, and neural networks. In the proposed algorithm, the Haar wavelet transform is used for low frequency components allocation, noise elimination and feature extraction. The principal component analysis is used to reduce the dimension of the extracted features. We use a set of different multi-layer neural networks as classifiers for each character; the inputs are represented by a reduced set of features. One of the key features of the proposed approach is creating a separate neural network for each type of character. The experimental results show that the proposed algorithm can effectively recognize the characters in images in the presence of impulse noise; the results are comparable with ABBYY FineReader and Tesseract OCR.