Online Handwritten Devanagari Stroke Recognition Using Extended Directional Features
This addresses the challenge of poor stroke recognition in handwritten Indian language scripts, which is incremental as it builds on existing methods with a new feature set.
The paper tackled the problem of recognizing online handwritten strokes for Devanagari script, achieving about 65% stroke-level recognition accuracy on a writer-independent dataset.
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.