An Unconstrained and Effective Approach of Script Identification for Online Bilingual Handwritten Text

Abstract

A number of monolingual online handwriting recognition (OHR) systems were proposed by researchers for languages like English, Chinese, Japanese, Hindi, Punjabi, Tamil, Bangla, Arabic and many more. But very few researchers have worked on bilingual or multilingual OHR systems, as the challenging part of these systems is the identification of scripts during recognition of intermixed words. In this paper, a bilingual OHR system is proposed for the mixed text containing words, written by using two scripts Gurmukhi (for Punjabi language) and Roman (for English language). For converting handwritten intermixed text to digital text, two steps have been implemented: first, to identify the script of segmented handwritten word and second, to run the respective script recognition engine. Multilayered perceptron neural network classifier along with directional code feature has been implemented to identify the script of segmented handwritten word. Considerable results have been observed for the script identification process.