Indexing large class handwritten character database

This paper proposes a method of indexing handwritten characters of a large number of classes by the use of Kd-tree. The Ridgelets and Gabor features are used for the purpose of representation. A multi dimensional feature vectors are further projected to a lower dimensional feature space using PCA. The reduced dimensional feature vectors are used to index the character database by Kd-tree. In a large class OCR system, the aim is to identify a character from a large class of characters. Interest behind this work is to have a quick reference to only those potential characters which can have a best match for given unknown character to be recognized without requiring scanning of the entire database. The proposed method can be used as a supplementary tool to speed up the task of identification. The proposed method is tested on handwritten Kannada character database consisting of 2000 images of 200 classes. Experimental results show that the approach yields a good Correct Index Power (CIP) and also depicts the effectiveness of the indexing approach