Classification of North and South Indian Handwritten Scripts using Gabor Wavelet Features

Abstract

Objectives: Handwritten script identification plays a vital role in processing handwritten data electronically. Most of the methods fail to provide accuracy due to variation in handwriting, hence the classification of the Indic script before providing it to OCR is crucial. The anticipated work helps increase the accuracy by categorizing the handwritten documents as north or South Indic script before further classification. Methods: This study has proposed a method, using Gabor filters to extract features from the text image for recognizing the kind of script, and seven widely used Indian scripts were considered for this experiment. The handwritten documents were collected from distinct individuals on request, under supervision. The database was manually created by extracting portions of lines from the scanned document images. Findings: A recognition accuracy of 100% was obtained for classifying North and South scripts while an average accuracy of 92% was obtained for biscript classification using KNN classifier at a portion of the line level. Novelty: The proposed method improves the accuracy by acting as a pre-processor to the OCR system by classifying the script according to North Indian script or South Indian Script. Further, it can be processed to find out the script type within the North or South Indian Scripts. Keywords: Handwritten Script; Gabor Filter; KNN Classifier; OCR; Indic Script