Handwritten script identification from a bi-script document at line level using Gabor filters

Abstract

In a country like India where more number of scripts are in use, automatic identification of printed and handwritten script facilitates many important applications including sorting of document images and searching online archives of document images. In this paper, a Gabor feature based approach is presented to identify different Indian scripts from handwritten document images. Eight popular Indian scripts are considered here. Features are extracted from pre-processed images, consisting of portion of a line extracted manually from a handwritten document, using Gabor filters. Script classification performance is analyzed using the k-nearest neighbor classifier (KNN). Experiments are performed using five-fold cross validation method. Excellent recognition rate of 100% is achieved for data set size of 100 images for each script.