Recognition of Kannada Handwritten Words using SVM Classifier with Convolutional Neural Network

In an area of handwriting recognition, many algorithms for recognition are proposed for Indian document images of Latin and Devanagari scripts. These documents generally lack in their layout organizations and low print quality. In order to overcome these drawbacks, a character segmentation algorithm is proposed for kannada handwriting recognition. In this work, a primary segmentation paths are obtained using structural property of characters, whereas overlapped and joined characters are separated using graph distance theory. Finally, segmentation results are validated using Support Vector Machine (SVM) classifier. Comprehensive simulation is carried out on different databases containing printed as well as handwritten texts. Benchmarking results illustrate that the proposed algorithms have better performance in terms of accuracy and sensitivity compared to other conventional approaches.