Abstract
Accuracy in handwritten character recognition system is a challenge in the area of pattern recognition because of a variety of writing styles. Eigenface is a method that has been widely used in face recognition systems. This method is proposed in the field of handwritten character recognition, in this paper. Here, Eigencharacters are created from a 2-D training set of images and weight vectors are generated. These weight vectors are used as feature vectors for classification. The classification is performed using Euclidean Distance, k-NN and SVM classifiers. Experimental results proved that the proposed Eigencharacter method using Euclidean distance produced good classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Meenu M and Jyothi RL, “Handwritten Character Recognition: A Comprehensive Review on Geometrical Analysis”, IOSR Journal of Computer Engineering (IOSR -JCE), Vol 17(2), 2015, Page(s): 83–88.
Thierry A, Sanparith M and Patrick G, “Online Handwritten Shape Recognition Using Segmental Hidden Markov Models”, IEEE Trans. On Pattern Analysis And Machine Intelligence, Vol 29(2), 2007, Page(s): 205–217.
Ashlin Deepa RN and Rajeswara Rao R, “Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 3(1), 2014, Page(s):481–485.
Huang, Kevin, “Principal Component Analysis in the Eigenface Technique for Facial Recognition”. Senior Theses, Trinity College, Hartford, CT 2012.
Ovidiu Ghita and Paul F Whalen, “Object recognition using eigenvectors”, Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, 85 (September 26, 1997); doi:10.1117/12.290331.
Peter Kajenski, “Handwritten Character Recognition in Ancient Manuscripts”, STANFORD CS 229 PROJECT, December 14, 2012.
Tao Xiang and Shaogang Gong, “Spectral clustering with eigenvector selection”, Pattern Recognition 41, 2008, Page(s): 1012–1029.
Nikos Karampatziakis and Paul Mineiro, “Discriminative Features via Generalized Eigenvectors”, Proceedings of the 31 st International Conference on Machine Learning, Beijing, China, W&CP Vol 32, 2014. JMLR.
Bunea, F. and Xiao, L. On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA. ArXiv e-prints, December 2012.
Manal Abdullah, Majda Wazzan and Sahar Bo-saeed, “Optimizing Face Recognition Using PCA”, International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 3(2), March 2012.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Ashlin Deepa, R.N., Rajeswara Rao, R. (2017). An Eigencharacter Technique for Offline-Tamil Handwritten Character Recognition. In: Mandal, J., Satapathy, S., Sanyal, M., Bhateja, V. (eds) Proceedings of the First International Conference on Intelligent Computing and Communication. Advances in Intelligent Systems and Computing, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-2035-3_51
Download citation
DOI: https://doi.org/10.1007/978-981-10-2035-3_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2034-6
Online ISBN: 978-981-10-2035-3
eBook Packages: EngineeringEngineering (R0)