Handwritten Tamil Character Recognition Using RCS Algorithm

Abstract

Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size and orientation angle of the characters. The scanned image is segmented into paragraphs using spatial space detection technique, paragraphs into lines using vertical histogram, lines into words using horizontal histogram, and words into character image glyphs using horizontal histogram. The extracted features considered for recognition are given to Support Vector Machine, Self Organizing Map, RCS, Fuzzy Neural Network and Radial Basis Network. Where the characters are classified using supervised learning algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts. This character recognition finds applications in document analysis where the handwritten document can be converted to editable printed document. Structure analysis suggested that the proposed system of RCS with back propagation network is given higher recognition rate.