Abstract
In this paper, we are exploring various features and classifiers for writer identification in light of Gurmukhi text handwriting. The identification of the writers based on a piece of handwriting is a challenging task for pattern recognition. The writer identification framework proposed in this paper includes diverse stages like image preprocessing, feature extraction, training, and classification. The framework first prepares a skeleton of the character so that meaningful data about the handwriting of writers can be extracted. The feature extraction stage incorporates various plans, namely, zoning, diagonal, transition, intersection and open end points, centroid, the horizontal peak extent, the vertical peak extent, parabola curve fitting, and power curve fitting based features. In order to assess the prominence of these features, we have used four classification techniques, namely, Naive Bayes, Decision Tree, Random Forest and AdaBoostM1. For experimental results, we have collected 49,000 samples from 70 different writers. In this work, maximum accuracy of 81.75% has been obtained with centroid features and AdaBoostM1 classifier.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Breiman L 2001 Random Forests, Machine Learning, 45(1):5–32.
Gazzah S and Amara N B 2008 Neural networks and support vector machines classifiers for writer identification using Arabic script, The International Arab Journal of Information Technology, 5(1): 92–101.
Ghiasi G and Safabakhsh R 2010 An efficient method for offline text independent writer identification, In Proceedings of the 20th International Conference on Pattern Recognition, 1245–1248.
John G H and Langley P 1995 Estimating Continuous Distributions in Bayesian Classifiers, In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 338–345.
Kumar M, Sharma R K and **dal M K 2013 A Novel Feature Extraction Technique for Offline Handwritten Gurmukhi Character Recognition, IETE Journal of Research, 59(6):687–692.
Kumar M, **dal M K and Sharma R K 2014a A Novel Hierarchical Techniques for Offline Handwritten Gurmukhi Character Recognition, National Academy Science Letters, 37(6):567–572.
Kumar M, Sharma R K and **dal M K 2014b Efficient Feature Extraction Techniques for Offline Handwritten Gurmukhi Character Recognition, National Academy Science Letters, 37(4):381–391.
Leclerc F, Plamondon R 1994 Automatic signature verification: the state of the art 1989–1993, International Journal of Pattern Recognition and Artificial Intelligence, 8(3):643–660.
Leeham G, Chachra S 2003 Writer identification using innovative binarised features of handwriting numerals, In the Proceedings of the 7 th International Conference on Document Analysis and Recognition (ICDAR).
Maadeed S A 2012 Text-dependent writer identification for Arabic Handwriting, Journal of Electrical and Computer Engineering, 13: 1–8.
Plamondon R, Lorette G 1989 Automatic Signature Verification and Writer Identification The State of the Art, Pattern Recognition, 22(2):107–131.
Schlapbach A, Bunke H 2005 Writer identification using an HMM based hand writing recognition system: to normalize the input or not?, In the Proceedings of 12th Conference of the International Graphonomics Society, Salerno, Italy.
Schlapbach A, Bunke H 2007 A writer identification and verification system using HMM based recognizers, Pattern Analysis and Application, 10(1):33–43.
Zois E, Anastassopoulos V 2000 Morphological Waveform Coding for Writer Identification, Pattern Recognition, 33(3):385–398.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sakshi, Garg, N.K., Kumar, M. (2018). Writer Identification System for Handwritten Gurmukhi Characters: Study of Different Feature-Classifier Combinations. In: Chaki, N., Cortesi, A., Devarakonda, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-6319-0_11
Download citation
DOI: https://doi.org/10.1007/978-981-10-6319-0_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6318-3
Online ISBN: 978-981-10-6319-0
eBook Packages: EngineeringEngineering (R0)