Template matching method for Kannada Handwritten recognition based on correlation analysis

Handwriting recognition systems have been developed out of a need to automate the process of converting data into electronic format, which otherwise would have been lengthy and error prone. As we all know that building a character recognition system is one of the major areas of research over a decade, due to its wide range of prospects. Various techniques have been discussed by many researchers regarding the recognition of handwritten characters for different languages. In this paper we adopted a Correlation Technique for recognition of Kannada Handwritten Characters. The formation of Kannada Characters into its compound form, also called as Kagunita makes its recognition more complex. The digitized input image is subjected to various preprocessing techniques and the processed image is then segmented into individual characters using simple segmentation algorithm. The segmented individual character is correlated with the stored templates. The template with maximum correlation value is displayed in editable format.