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Proceedings Paper

Application of back-propagation to the recognition of handwritten digits using morphologically derived features
Author(s): Daniel J. Hepp
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Paper Abstract

This paper presents results of an experiment on handwritten digit recognition using a four-layered backpropagation network. The input to the network is a 105 element feature vector formed in two steps. First, morphological operations are performed on the input digit to create images of six different cavity features. Then, along with the normalized digit image, each of the cavity images is coarse-coded to produce the input vector. The network is trained on 5200 normalized, repaired digits and is tested on two other large sets. All digit samples were obtained from the United States Postal Service. The first test set, composed of 1916 digits, is used to select a decision strategy for the network which maximizes correct recognition rate while keeping the error rate under one percent. This strategy is then applied to the second test set, a true test set composed of 3568 characters, with recognition rate near 97 percent and an error rate of less than one percent. These results suggest that the use of morphologically derived features in backpropagation networks is effective for optical character recognition.

Paper Details

Date Published: 1 April 1991
PDF: 6 pages
Proc. SPIE 1451, Nonlinear Image Processing II, (1 April 1991); doi: 10.1117/12.44328
Show Author Affiliations
Daniel J. Hepp, Environmental Research Institute of Michigan (United States)


Published in SPIE Proceedings Vol. 1451:
Nonlinear Image Processing II
Edward R. Dougherty; Gonzalo R. Arce; Charles G. Boncelet, Editor(s)

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