Comparison of neural network classifiers for optical character recognition

Thomas E. Baker; Hal McCartor

doi:10.1117/12.130287

1 August 1992 Comparison of neural network classifiers for optical character recognition

Thomas E. Baker, Hal McCartor

Proceedings Volume 1661, Machine Vision Applications in Character Recognition and Industrial Inspection; (1992) https://doi.org/10.1117/12.130287
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

Abstract

The recognition of handwritten characters is an important technology for document processing and for advanced user interfaces. Recent advances in artificial neural network (ANN) classifiers have shown impressive pattern recognition results when using noisy data. One advantage of ANN algorithms is that they are parallel by design, which allows a natural implementation on high-speed parallel architectures. The availability of standard databases of handwritten characters permits a fair comparison between different OCR classifiers. This paper compares the classification performance of two popular ANN algorithms: Back Propagation and Learning Vector Quantization. A set of digits from the National Institute of Standards and Technology''s Handwritten Database is used to test the two classifiers. Each algorithm''s execution time and memory efficiency is also compared, based on an implementation for Adaptive Solutions'' highly parallel CNAPS architecture. We also show that a fair comparison cannot be made between OCR research that does not use the same set of characters for testing.

Citation Download Citation

Thomas E. Baker and Hal McCartor "Comparison of neural network classifiers for optical character recognition", Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); https://doi.org/10.1117/12.130287

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