Adaptive Zoning Design by Supervised Learning using Multi-objective Optimization

Zoning is a widespread feature extraction technique for handwritten digit recognition, since it is able to handle handwritten pattern variability. Static techniques for zoning design have recently been superseded by adaptive techniques, in which zoning design is considered as the result of an optimization procedure. This paper presents a new learning strategy to optimal zoning design using multi-objective genetic algorithm. More precisely, the nondominant sorting genetic algorithm II (NSGA II) has been applied to define, in a single process, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests, carried out in the field of handwritten digit recognition, show the effectiveness of this new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

[1]  Anil K. Jain,et al.  Feature extraction methods for character recognition-A survey , 1996, Pattern Recognit..

[2]  Sameer Singh,et al.  Cursive digit and character recognition in CEDAR database , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Ching Y. Suen,et al.  Analysis and recognition of alphanumeric handprints by parts , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[4]  K. M. Kulkarni,et al.  A high accuracy algorithm for recognition of handwritten numerals , 1988, Pattern Recognit..

[5]  Majid Ahmadi,et al.  Handwritten numeral recognition with multiple features and multistage classifiers , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[6]  Marc Parizeau,et al.  Genetic engineering of hierarchical fuzzy regional representations for handwritten character recognition , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[7]  Atul Negi,et al.  Localization, extraction and recognition of text in Telugu document images , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[8]  Alexander Filatov,et al.  The AddressScriptTM Recognition System for Handwritten Envelopes , 1998, Document Analysis Systems.

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

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

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Ernest Valveny,et al.  Numerical recognition for quality control of surgical sachets , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[13]  Ching Y. Suen,et al.  Computer recognition of unconstrained handwritten numerals , 1992, Proc. IEEE.

[14]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[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]  Luiz Eduardo Soares de Oliveira,et al.  Intelligent zoning design using multi-objective evolutionary algorithms , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[17]  Sung-Hyuk Cha,et al.  Optimizing binary feature vector similarity measure using genetic algorithm and handwritten character recognition , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[18]  Majid Ahmadi,et al.  Recognition of handwritten numerals with multiple feature and multistage classifier , 1995, Pattern Recognit..

[19]  Sebastiano Impedovo,et al.  Zoning Methods for Hand-Written Character Recognition: An Overview , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[20]  Mindy Bokser,et al.  Omnidocument technologies , 1992, Proc. IEEE.

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

[22]  Ching Y. Suen,et al.  Sorting and Recognizing Cheques and Financial Documents , 1998, Document Analysis Systems.

[23]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[24]  Fumitaka Kimura,et al.  Handwritten numerical recognition based on multiple algorithms , 1991, Pattern Recognit..

[25]  Horst Bunke,et al.  Off-Line, Handwritten Numeral Recognition by Perturbation Method , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Sebastiano Impedovo,et al.  Optimal zoning design by genetic algorithms , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.