期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：4
页码：4887-4891
出版社：TechScience Publications
摘要：Automatic recognition of handwritten characters is a difficult task because characters are written in various curved & cursive ways, so they could be of different sizes, orientation, thickness, format and dimension. An offline handwritten Hindi character recognition system using neural network is presented in this paper. Neural networks are good at recognizing handwritten characters as these networks are insensitive to the missing data. The paper proposes the approach to recognize Hindi characters in four stages— 1) Scanning, 2) Preprocessing, 3) Feature Extraction and, 4) Recognition. Preprocessing includes noise reduction, binarization, normalization and thinning. Feature extraction includes extracting some useful information out of the thinned image in the form of a feature vector. The feature vector comprises of pixels values of normalized character image. A Backpropagation neural network is used for classification. Experimental result shows that this approach provides better results as compared to other techniques in terms of recognition accuracy, training time and classification time. The average accuracy of recognition of the system is 93%
