Online Handwritten Devanagari Stroke Recognition Using Extended Directional Features

TL;DR

This paper introduces the extended directional features (EDF), a new feature set for online handwritten stroke recognition in Devanagari script, achieving over 65% accuracy in writer-independent recognition tasks.

Contribution

The paper proposes the EDF feature set specifically designed for Devanagari stroke recognition, demonstrating its effectiveness in writer-independent recognition scenarios.

Findings

EDF achieves about 65+% stroke recognition accuracy

Stroke recognition performance improves with EDF over previous features

Effective for writer-independent handwritten Devanagari recognition

Abstract

This paper describes a new feature set, called the extended directional features (EDF) for use in the recognition of online handwritten strokes. We use EDF specifically to recognize strokes that form a basis for producing Devanagari script, which is the most widely used Indian language script. It should be noted that stroke recognition in handwritten script is equivalent to phoneme recognition in speech signals and is generally very poor and of the order of 20% for singing voice. Experiments are conducted for the automatic recognition of isolated handwritten strokes. Initially we describe the proposed feature set, namely EDF and then show how this feature can be effectively utilized for writer independent script recognition through stroke recognition. Experimental results show that the extended directional feature set performs well with about 65+% stroke level recognition accuracy for writer independent data set.