Handwritten digit recognition based on prototypes created by Euclidean distance

Handwritten digits are recognized using prototypes created by a training algorithm based on the Euclidean distance. The subsequent classification of a handwritten digit is based on criteria considering the Euclidean distance to the prototypes. A training set of 2361 patterns is used to create the prototypes and a separate set of 1320 patterns is used to test the proposed method. The system performance is compared to two other known classification algorithms: a MLP (multilayer perceptron network), and SOM (self-organizing map) plus LVQ1 (a linear vector quantization algorithm). The proposed method reached a recognition rate of 93.5% when using the nearest-prototype criterion, and raised to 94.8% when using a nearest-prototype-voting criterion. It compared favorably with the MLP (91.8%) and SOM+LVQ1 (91.5%).

[1]  Robert J. Schalkoff,et al.  Pattern recognition : statistical, structural and neural approaches / Robert J. Schalkoff , 1992 .

[2]  Yoshihiko Hamamoto,et al.  On the Behavior of Artificial Neural Network Classifiers in High-Dimensional Spaces , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[4]  Zheru Chi,et al.  Handwritten numeral recognition using self-organizing maps and fuzzy rules , 1995, Pattern Recognit..

[5]  C.A. Perez,et al.  Improvements on handwritten digit recognition by genetic selection of neural network topology and by augmented training , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[6]  P. Nagabhushan,et al.  A three-dimensional neural network model for unconstrained handwritten numeral recognition: a new approach , 1998, Pattern Recognit..

[7]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic , 1995 .

[8]  Jung-Hsien Chiang,et al.  Neural and Fuzzy Methods in Handwriting Recognition , 1997, Computer.

[9]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[10]  Sung-Bae Cho,et al.  Neural-network classifiers for recognizing totally unconstrained handwritten numerals , 1997, IEEE Trans. Neural Networks.

[11]  Stamatios V. Kartalopoulos.  Neural Network Paradigms , 1996 .

[12]  Claudio A. Perez,et al.  Improvements on handwritten digit recognition by cooperation of modular neural networks , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).