Handwritten Gurmukhi Character Recognition Using Wavelet Transforms

This paper presents an OCR (optical character recognition) system for the handwritten Gurmukhi characters using different wavelet transforms. There is a lot of research work available in case of optical character recognition of various languages like English, Chinese, and Arabic etc. But in case of handwritten Gurmukhi script very less research has been done. In this paper, Different Wavelet transforms have been used for feature extraction. Also zonal densities of different zones of an image have been used in the feature set. In this work, 50 samples of each character have been used. The back propagation neural network has been used for classification. An average recognition accuracy of 81.71% has been achieved.