Offline handwritten Gujarati numeral recognition using low-level strokes

Abstract

This paper focuses on the development of offline handwritten Gujarati numeral database of reasonable size and its recognition using low-level stroke features. The database consists of 14,000 samples collected from 140 people with different age group, educational background, and work culture. A novel technique for the extraction of various low-level stroke features, like endpoints, junction points, line segments, and curve segments, is proposed, and the block-wise histogram of low-level stroke features is used for the recognition of offline handwritten numerals from two of the popular Indian scripts, namely Gujarati and Devanagari. The baseline experiments were performed using k-nearest neighbour (k-NN) classifier, and the results were further improved by using the statistically advance support vector machine (SVM) classifier with radial basis function (RBF) kernel. The average test accuracy obtained on Gujarati and Devanagari database were 98.46% and 98.65%, respectively, which is comparable to other existing work. The experiments were also performed on the mixed numerals recognition from Gujarati-Devanagari and Gujarati-English considering the multi-script scenarios in Indian documents.