Paper Info
Title
Recognition of offline handwriten Devanagari numerals using regional weighted run length features
Abstract
Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-validation scheme, the proposed system yields the highest recognition accuracy of 95.02% using Support Vector Machine (SVM) classifier.
Year
DOI
Venue
2018
10.1109/ICCECE.2016.8009567
2016 International Conference on Computer, Electrical & Communication Engineering (ICCECE)
Keywords
Field
DocType
Handwritten Digit Recognition,Regional Weighted Run Length features,Devanagari digits,Support Vector Machine
Devanagari,Pattern recognition,Computer science,Numerical digit,Support vector machine,Speech recognition,Latin script,Artificial intelligence,Digit recognition,Classifier (linguistics),Numeral system
Journal
Volume
ISSN
ISBN
abs/1806.11517
1st IEEE International Conference on Computer, Electrical and Communication Engineering (ICCECE 2016)
978-1-5090-4433-7
Citations 
PageRank 
References 
0
0.34
2
Authors
4
Name
Order
Citations
PageRank
Pawan Kumar Singh15714.89
Supratim Das212.08
Ram Sarkar342068.85
Mita Nasipuri4725107.01