Statistical Texture Features based Handwritten and Printed Text Classification in South Indian Documents

TL;DR

This paper presents a method for classifying handwritten and printed words in South Indian documents using statistical texture features and k-NN classifier, achieving high accuracy across multiple scripts.

Contribution

It introduces a novel approach combining statistical texture features with k-NN for word-level classification in South Indian scripts, including Roman script.

Findings

Achieved an average classification rate of 99.26% on South Indian scripts.

Validated approach across multiple datasets and scripts.

Extended method to Roman script with promising results.

Abstract

In this paper, we use statistical texture features for handwritten and printed text classification. We primarily aim for word level classification in south Indian scripts. Words are first extracted from the scanned document. For each extracted word, statistical texture features are computed such as mean, standard deviation, smoothness, moment, uniformity, entropy and local range including local entropy. These feature vectors are then used to classify words via k-NN classifier. We have validated the approach over several different datasets. Scripts like Kannada, Telugu, Malayalam and Hindi i.e., Devanagari are primarily employed where an average classification rate of 99.26% is achieved. In addition, to provide an extensibility of the approach, we address Roman script by using publicly available dataset and interesting results are reported.