Title :
Associative memories for handwritten pattern recognition
Author :
L?³pez-Aligu?©, Francisco J. ; Acevedo-Sotoca, Isabel ; Orellana, Carlos G. ; Velasco, Horacio G.
Author_Institution :
Dept. of Electron. & Electrochem. Eng., Univ. de Extremadura, Badajoz, Spain
Abstract :
We describe the construction of a complete system of handwritten classification on the basis of bidirectional associative memories (BAM). The BAM is synthesized with a two-layer neural network in which the neuron thresholds are adjusted at the moment of learning, and the ideal prototypes for each class are selected using an automatic method. The system is completed with a topological preprocessor that eliminates distortion and noise components. It was tested on the popular NIST#19 database, attaining 100% success rates for all the characters, even under conditions of high levels of contamination due to noise or distortion of the input image.
Keywords :
content-addressable storage; handwritten character recognition; image denoising; neural nets; NIST#19 database; bidirectional associative memories; distortion; handwritten pattern recognition; noise; topological preprocessor; two-layer neural network; Associative memory; Contamination; Image databases; Magnesium compounds; Network synthesis; Neural networks; Neurons; Pattern recognition; Prototypes; Testing;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223908
