Handwritten Script Identification from Text Lines

TL;DR

This paper presents a robust method for identifying handwritten scripts at the text line level in multilingual documents, using features like Chain Code Histogram and DFT, achieving over 95% accuracy across seven Indic scripts and Roman.

Contribution

It introduces a novel script identification approach combining CCH and DFT features with SVM classification for handwritten text lines.

Findings

Achieved 95.14% average identification accuracy.

Successfully distinguished seven Indic scripts and Roman script.

Validated robustness across diverse handwritten scripts.

Abstract

In a multilingual country like India where 12 different official scripts are in use, automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents, searching for documents on the web/digital archives containing a particular script and for the selection of script specific Optical Character Recognition (OCR) system in a multilingual environment. In this paper, we propose a robust method towards identifying scripts from the handwritten documents at text line-level. The recognition is based upon features extracted using Chain Code Histogram (CCH) and Discrete Fourier Transform (DFT). The proposed method is experimented on 800 handwritten text lines written in seven Indic scripts namely, Gujarati, Kannada, Malayalam, Oriya, Tamil, Telugu, Urdu along with Roman script and yielded an average identification rate of 95.14% using Support Vector Machine (SVM) classifier.