A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition

TL;DR

This paper introduces a novel script identification method using quad-tree decomposition and Gabor wavelet features at block level, achieving high accuracy for multiple Indic scripts.

Contribution

It presents a new approach combining quad-tree decomposition with Gabor wavelet features for script identification at block level, evaluated with multiple classifiers.

Findings

Achieved 96.86% accuracy with MLP classifier

Effective differentiation of 11 Indic scripts

Validated approach with 3-fold cross validation

Abstract

A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts. But in a country like India, where multi-script scenario is prevalent, identifying scripts beforehand becomes obligatory. In this paper, we present the significance of Gabor wavelets filters in extracting directional energy and entropy distributions for 11 official handwritten scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Tamil, Telugu, Urdu and Roman. The experimentation is conducted at block level based on a quad-tree decomposition approach and evaluated using six different well-known classifiers. Finally, the best identification accuracy of 96.86% has been achieved by Multi Layer Perceptron (MLP) classifier for 3-fold cross validation at level-2 decomposition. The results serve to establish the efficacy of the present approach to the classification of handwritten Indic scripts