Recognition of Offline Handwritten Devanagari Numerals using Regional Weighted Run Length Features
This addresses the recognition of Devanagari script, a popular script in India, where accuracy has been less satisfactory compared to Roman characters, representing an incremental improvement in a domain-specific task.
The paper tackled the problem of recognizing offline handwritten Devanagari numerals by proposing a system using novel 196-element Mask Oriented Directional features, achieving a highest recognition accuracy of 95.02% with an SVM classifier on 6000 digit samples.
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.