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

arXiv: https://arxiv.org/abs/1303.3087 · HTML: https://mlsanity.com/paper/1303.3087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2013.

## Built on by (most cited first, 1 of 1)

- [Handwritten and Printed Text Segmentation via Region-Aware Human-Writing Descriptor Engineering](https://mlsanity.com/paper/2607.15936/) (2026)

## Abstract (arXiv)

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.