10. Performance Evaluation of Different Neural Network Classifiers for Sanskrit Character Recognition

Handwritten character recognition (HCR) is one of the significant issues in today’s emerging world. It is very difficult to identify the characters from a handwritten document using optical character recognition (OCR) technique. In our work, geometrical feature extraction and neural network computational algorithm are used for recognizing the offline handwritten Sanskrit characters. Initially the binarization and denoising processes are performed on the scanned handwritten document. Later, skeletonization, skewness detection, and correction processes are performed. Image is segmented and required features are extracted and fed into the different classifiers for character recognition. Then, the comparative study of the Sanskrit character recognition is done by employing the RCS with BPNN, BPNN with RBF, and MLP. The proposed character recognition system deploys precision, mean square error rate, recall, false error rate, false-positive error rate, sensitivity, specificity, and accuracy for effective analysis of the handwritten Sanskrit characters.