A New Adaptive Zoning Technique for Handwritten Digit Recognition

In this paper we present a new adaptive zoning technique based on Voronoi tessellation for the task of handwritten digit recognition. This technique extracts features according to an optimal zoning distribution, obtained by an evolutionary-strategy based search. Several experiments have been conducted on the MNIST and the USPS datasets to investigate the proposed approach. Comparisons with regular square zoning reveal that the presented zoning strategy achieves better results for any type of SVM classifier. Furthermore, the proposed zoning method shows that the combination of the adaptive zoning strategy with the Voronoi topology leads to find a distribution of zones able to improve accuracy significantly. As a matter of fact reached accuracies are close to the best algorithms.

[1]  P. Vanaja Ranjan,et al.  Support Vector Machine based Handwritten Numeral Recognition of Kannada Script , 2009, 2009 IEEE International Advance Computing Conference.

[2]  Robert Sabourin,et al.  A Multi-objective Memetic Algorithm for Intelligent Feature Extraction , 2005, EMO.

[3]  E. Sackinger,et al.  Neural-Network and k-Nearest-neighbor Classifiers , 1991 .

[4]  Lokesh Kumar Sharma,et al.  Entropy Weighting Genetic k-Means Algorithm for Subspace Clustering , 2010 .

[5]  A. Papandreou,et al.  Adaptive Zoning Features for Character and Word Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[6]  Hans-Georg Beyer,et al.  The Theory of Evolution Strategies , 2001, Natural Computing Series.

[7]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .

[8]  Ching Y. Suen,et al.  A novel hybrid CNN-SVM classifier for recognizing handwritten digits , 2012, Pattern Recognit..

[9]  Deepika Gupta,et al.  Isolated Handwritten Digit Recognition using Adaptive Unsupervised Incremental Learning Technique , 2010 .

[10]  Yann LeCun,et al.  Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.

[11]  Sebastiano Impedovo,et al.  Membership Functions for Zoning-Based Recognition of Handwritten Digits , 2010, 2010 20th International Conference on Pattern Recognition.

[12]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[13]  Harris Drucker,et al.  Boosting Performance in Neural Networks , 1993, Int. J. Pattern Recognit. Artif. Intell..

[14]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[15]  Luiz Eduardo Soares de Oliveira,et al.  Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Ioannis Pratikakis,et al.  Hybrid off-line OCR for isolated handwritten Greek characters , 2007 .

[17]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Luiz Eduardo Soares de Oliveira,et al.  Handwritten Character Recognition Using Nonsymmetrical Perceptual Zoning , 2007, Int. J. Pattern Recognit. Artif. Intell..

[19]  Cheng-Lin Liu,et al.  Handwritten digit recognition: benchmarking of state-of-the-art techniques , 2003, Pattern Recognit..

[20]  Giuseppe Pirlo,et al.  New Advancements in Zoning-Based Recognition of Handwritten Characters , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[21]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[22]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[23]  Xiuzi Ye,et al.  A hybrid method for robust car plate character recognition , 2005, Eng. Appl. Artif. Intell..

[24]  Sebastiano Impedovo,et al.  Analysis of Membership Functions for Voronoi-Based Classification , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.