Structural Run Based Feature Vector to Classify Printed Tamil Characters Using Neural Network

Feature Extraction plays most crucial and important role in character recognition. The selection of stable and representative set of features is the main problem in pattern recognition. Because of font characteristics and style variation of machine printed Tamil characters, feature extraction remains a problem. Feature extraction involves reducing the amount of resources required to describe a set of data. In this paper, new method has been proposed to extract structural features from Machine printed Tamil characters using horizontal and vertical projections. Based on the structural properties of upper and lower modifiers, characters are divided into various categories and features are extracted accordingly. The extracted features from the real life degraded documents are classified to identify the characters. The system has been tested with printed Tamil characters and achieves 99.67% character recognition accuracy on average. Experimental results show that structure and category of the characters are identified by the proposed method for the regular characters of various sizes.