Abstract
Optical character recognition (OCR) is a type of document image analysis where scanned digital image that contains either machine printed or handwritten script input into an OCR software engine and translating it into an editable machine readable digital text format. In this paper we designed a novel and robust two stage recognition system for Odia handwritten characters as well as we prepare a standard deviation and zone centroid average distance based feature matrix for more accuracy while training and testing the Neural Network. The OHCR System is based on the algorithm of feed forward BPNN in two stage to perform the optimum feature extraction and recognition. The Odia characters are classified into four groups according to similarity of their shapes and features. The system uses ANN in two stages, having different parameters, the first stage classifies the characters into similar groups and in the second stage individual characters are recognized.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rejean, P., Srihari Sargur, N.: On-line and Off-line Handwriting Recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)
Prema, K.V., Subba, R.N.V.: Two-tier architecture for unconstrained handwritten character recognition. Sadhna 27, Part 5, 585–594 (2002)
Tripathy, N., Pal, U.: Handwriting segmentation of constrained Oriya text. Sadhna 31, Part 6, 755–769 (2006)
Rajashekararadhya, S.V., Vanaja Ranjan, P.: A Novel Zone Based Feature Extraction Algorithm for Handwritten Num Recognition of Four Indian Scripts. Digital Technology Journal 2, 41–51 (2009) ISSN 1802-5811
Heutte, L., Paquet, T., et al.: A structural statistical feature based vector for handwritten character recognition. Pattern Recognition Letters 19, 629–641 (1998)
Wang, X., Ding, X., Liu, C.: Gabor filters-based feature extraction for character recognition. Pattern Recognition Society (2004)
Pal, U., Roy, P.P.: Multi-oriented and curved text lines extraction from Indian documents. IEEE Trans. on Systems, Man and Cybernetics-Part B 34, 1676–1684 (2004)
Mohanty, S., Behera, H.K.: A complete OCR Development System for Oriya Script. In: Proceedings of SIMPLE 2004, IIT Kharagpur (2004)
Pal, U., Wakabayashi, T., Kimura, F.: A System for Off-line Oriya Handwritten Character Recognition using Curvature Feature. IEEE (2007) 0-7695-3068-0/07
Ren, J.: Multi-order Standard Deviation Based Distance Metrics and its Application in Handwritten Chinese Character Recognition. In: 18th International conference on Pattern Recognition (ICPR 2006). IEEE (2006)
Liu, H., Ding, X.: Handwritten Character Recognition Using Gradient Feature and Quadratic Classifier with Multiple Discrimination Schemes. In: Proceedings of the 2005 Eighth International Conference on Document Analysis and Recognition (ICDAR 2005). IEEE (2005)
Blumenstein, M., Verma, B., Basli, H.: A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, ICDAR 2003. IEEE (2003) 0-7695-1960-1/03
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Padhi, D., Senapati, D. (2013). Zone Centroid Distance and Standard Deviation Based Feature Matrix for Odia Handwritten Character Recognition. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_73
Download citation
DOI: https://doi.org/10.1007/978-3-642-35314-7_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35313-0
Online ISBN: 978-3-642-35314-7
eBook Packages: EngineeringEngineering (R0)