Recognition of Offline Handwritten Devanagari Numerals using Regional Weighted Run Length Features

TL;DR

This paper introduces a new feature extraction method for recognizing handwritten Devanagari numerals, achieving high accuracy with a novel feature set and multiple classifiers.

Contribution

It proposes a novel 196-element Mask Oriented Directional feature set specifically for Devanagari digit recognition, improving accuracy over existing methods.

Findings

Achieved 95.02% recognition accuracy with SVM classifier

Validated effectiveness using 6000 handwritten samples

Demonstrated superiority over conventional features

Abstract

Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-validation scheme, the proposed system yields the highest recognition accuracy of 95.02% using Support Vector Machine (SVM) classifier.