Title :
Online handwritten devanagari stroke recognition using extended directional features
Author :
Lajish, V.L. ; Kopparapu, Sunil Kumar
Author_Institution :
Dept. of Comput. Sci., Univ. of Calicut, Calicut, India
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.
Keywords :
handwritten character recognition; natural language processing; Devanagari script; EDF; Indian language script; extended directional features; feature set; online handwritten Devanagari stroke recognition; phoneme recognition; singing voice; speech signals; stroke level recognition accuracy; writer independent data set; writer independent script recognition; Accuracy; Character recognition; Feature extraction; Handwriting recognition; Speech; Speech recognition; Training;
Conference_Titel :
Signal Processing and Communication Systems (ICSPCS), 2014 8th International Conference on
Conference_Location :
Gold Coast, QLD
DOI :
10.1109/ICSPCS.2014.7021063