Training neocognitron to recognize handwritten digits in the real world

Using a large-scale real-world database-the ETL-1 database of the Electrotechnical Laboratory in Japan-we show that a neocognitron trained by unsupervised learning with a winner-take-all process can recognize handwritten digits with a recognition rate higher than 97%. We use the technique of dual th...

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Bibliographic Details
Published inProceedings of IEEE International Symposium on Parallel Algorithms Architecture Synthesis pp. 292 - 298
Main Authors Fukushima, K., Nagahara, K., Shouno, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Data engineering
Feature extraction
Handwriting recognition
Large-scale systems
Neural networks
Pattern recognition
Robustness
Spatial databases
Unsupervised learning
Visual system
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Summary:Using a large-scale real-world database-the ETL-1 database of the Electrotechnical Laboratory in Japan-we show that a neocognitron trained by unsupervised learning with a winner-take-all process can recognize handwritten digits with a recognition rate higher than 97%. We use the technique of dual thresholds for feature-extracting S-cells, and higher threshold values are used in the learning than in the recognition phase. We discuss how the threshold values affect the recognition rate. The learning method for the cells of the highest stage of the network has been modified from the conventional one, in order to reconcile the unsupervised learning procedure with the use of information about the category names of the training patterns.
ISBN:0818678704
9780818678707
DOI:10.1109/AISPAS.1997.581680