Paper Info
Title
Ctrl -Capturedlight: A Novel Feature Descriptor For Online Assamese Numeral Recognition
Abstract
Online handwriting recognition (OHR) has gained major research interest not just due to the enormous technological advancement in recent years, but also the easy availability of the various electronic devices. This digital revolution is opening up a new dimension in every passing day to the regional and low resource languages with these languages get noticed by the researchers. In this paper, we have targeted a low resource language, Assamese, which is mainly spoken in the eastern region of India. We have proposed a novel and efficient feature vector for recognition of online handwritten Assamese numeral images. Our feature vector has been conceptualized based on the properties of light rays emerging out from a point source. Here we consider that there are multiple hypothetical light emerging sources in a sample numeral image. The amount of light fenced by the image is quantified and considered as a feature. The idea of using point light source to estimate the shape of online handwritten numerals is completely new and efficient. Impressive recognition accuracy is obtained on application of the feature vector on a standard online handwritten Assamese numeral database and it outnumbers some popular and standard feature descriptors, available in the literature. The source code of this work can be found in the following github link: https://github.com/ghoshsoulib/CTRLAssamese-Digit-Recognition.
Year
DOI
Venue
2021
10.1007/s11042-020-10081-7
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Numeral recognition, Online handwriting, Assamese, Shape based feature, Light ray
Journal
80
Issue
ISSN
Citations 
20
1380-7501
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Soulib Ghosh122.40
Agneet Chatterjee232.74
Shibaprasad Sen373.89
Neeraj Kumar42889236.13
Ram Sarkar542068.85