A novel framework for writer identification based on pre-segmented Gurmukhi characters

Handwriting is an obtained apparatus utilized for correspondence of one’s recognition or sentiments. Components that judge a person’s handwriting is not merely subject to the individual’s handwriting depends on the background, additionally considers like nervousness, inspiration and the reason for t...

Full description

Saved in:
Bibliographic Details
Published in:Sadhana (Bangalore) 2018-12, Vol.43 (12), p.1-9, Article 197
Main Authors: Kumar, Munish, Jindal, M K, Sharma, R K, Jindal, Simpel Rani
Format: Article
Language:English
Subjects:
Classifiers
Engineering
Feature extraction
Handwriting
Handwriting recognition
Writers
Zoning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Handwriting is an obtained apparatus utilized for correspondence of one’s recognition or sentiments. Components that judge a person’s handwriting is not merely subject to the individual’s handwriting depends on the background, additionally considers like nervousness, inspiration and the reason for the handwriting. In spite of the high variation, in a man’s handwriting, recent outcomes from various writers have demonstrated that it has adequate individual quality to be utilized as an identification strategy. In this paper, the authors are the pact with a novel approach to text dependent writer identification in view of pre-segmented Gurmukhi characters. The text dependent writer identification framework proposed in this paper includes distinctive stages like preprocessing, feature extraction, classification or identification. The feature extraction stage incorporates four schemes, zoning, diagonal, transitions and peak extent based features. To analyze the proposed framework execution, experiments are performed with two classifiers, namely, k-NN and SVM. SVM is also considered with linear-kernel in the present work. For experimental results, we have collected 31,500 samples from 90 different writers for 35 class problem. Maximum writer identification accuracy of 89.85% has been achieved by using a combination of zoning, transition and peak extent based features with Linear-SVM classifier when we have taken 70% data as the training set and remaining 30% data as the testing set. Using 10-fold cross validation, we have achieved an accuracy of 94.76% with a combination of zoning, transition and peak extent based features and Linear-SVM classifier.
ISSN:0256-2499
0973-7677
DOI:10.1007/s12046-018-0966-z