Paper Info
Title
Performance Comparison of Several Feature Selection Techniques for Offline Handwritten Character Recognition
Abstract
This paper presents a performance comparison of various feature selection techniques for offline handwritten Gurmukhi character recognition. Research on offline handwritten character recognition of Gurmukhi script is very difficult due to the complex structural properties of the script that are not matter-of-fact in most other scripts. Gurmukhi is the script used for writing the Punjabi language, which is the official language of Punjab state in India. We have presented a feature extraction technique for offline handwritten Gurmukhi character recognition based on the boundary extent of the character image and used various feature selection techniques, to reduce the dimensionality of feature vectors. We have also compared their recognition performances using two different classifiers, namely, Nearest Neighbours (NN) and Support Vector Machine (SVM) with linear kernel. Different classification schemes measures are used for the performance analysis of different feature selection techniques. Results obtained using presented feature extraction technique show that Chi Squared Attribute (CSA) feature selection technique performs better than other feature selection techniques using NN and SVM with linear kernel classifier for character recognition. In this work, we have obtained zone wise maximum recognition accuracy of 88.3%, 95.2% and 91.3% for upper zone, middle zone and lower zone of Gurmukhi script, respectively.
Year
DOI
Venue
2018
10.1109/RICE.2018.8509076
2018 International Conference on Research in Intelligent and Computing in Engineering (RICE)
Keywords
DocType
ISBN
Handwritten character recognition,Feature extraction,Feature selection,Classification,NN,SVM
Conference
978-1-5386-2600-9
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
Munish Kumar1175.22
Manish Kumar Jindal2125.37
Rajendra Kumar Sharma3359.62
Simpel Rani Jindal400.34