Recognition of online handwritten Gurmukhi characters based on zone and stroke identification

Abstract

Handwriting recognition is a technique that converts handwritten characters into a machine-processable format. Handwritten characters can either be presented to machine online or offline. A good amount of research in this area has been carried out for English, Chinese, Japanese and Korean languages. Research is also going on for Indian languages on developing online handwriting recognition systems. Headline and baseline are common features in most Indic languages which divide a character into three zones, namely, upper, middle and lower zones. Identification of headline and baseline is a major task for classification of strokes located in these three zones. A zone identification algorithm is proposed and tested in this text for online handwriting recognition of Gurmukhi script. The strokes are grouped into these separate zones and are recognized based on respective support vector machine model for each zone. A rule-based approach has also been applied and tested for generation of characters from the set of recognized strokes. In this work, an accuracy of 95.3% has been achieved for zone identification and an accuracy of 74.8% has been achieved for character identification for Gurmukhi script. This accuracy has been achieved when the recognition engines of three zones were tested on the dataset of 428 characters each written by 10 users.