NDLI: Online handwritten devanagari stroke recognition using extended directional features

Content Provider	IEEE Xplore Digital Library
Author	Lajish, V. L. Kopparapu, Sunil Kumar
Copyright Year	2014
Description	Author affiliation: TCS Innovations Lab - Mumbai, Tata Consultancy Services Limited, Thane (West), Maharashtra, India (Kopparapu, Sunil Kumar) \|\| Department of Computer Science, University of Calicut, Kerala - 673635, India (Lajish, V L)
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.
Starting Page	1
Ending Page	5
File Size	535911
Page Count	5
File Format	PDF
ISBN	9781479952557
DOI	10.1109/ICSPCS.2014.7021063
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Handwriting recognition Accuracy Speech recognition Speech Feature extraction Character recognition
Content Type	Text
Resource Type	Article

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