Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

Some Approaches to the Recognition of Handwritten Numerals

The present work deals with the recognition of handwritten isolated numerals by utilizing a recent approach, which aims at tackling the variability in the writing styles. A two pronged approach involving pre-classification and the recognition has been followed in this paper. For the pre-classification of numerals, two approaches have been presented. The first is heuristic based and the second is stroke based. A recent feature extraction method, namely sector data method, which takes care of variability in the handwritten numerals, has been incorporated into the system thus the variability involved in the writing styles of different individuals is taken care of by extracting features from the sector based approach. The back propagation neural networks have been used in the recognition process using the features extracted from the sector-based approach. On the basis of recognition rates obtained with samples written by different individuals, it is concluded that the sector based approach is better suited for the recognition of numerals when pre-classification is made on the basis of strokes