NDLI: Associative memories for handwritten pattern recognition

Content Provider	IEEE Xplore Digital Library
Author	Lopez-Aligue, F.J. Acevedo-Sotoca, I. Orellana, C.G. Velasco, H.G.
Copyright Year	2003
Description	Author affiliation: Dept. of Electron. & Electrochem. Eng., Univ. de Extremadura, Badajoz, Spain (Lopez-Aligue, F.J.; Acevedo-Sotoca, I.; Orellana, C.G.; Velasco, H.G.)
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.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1441
Ending Page	1446
File Size	432136
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223908
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Pattern recognition Magnesium compounds Network synthesis Neural networks Neurons Prototypes Testing Image databases Contamination
Content Type	Text
Resource Type	Article

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