Comparison of class-selective rejection rules for OCR

Thien M. Ha

doi:10.1117/12.270069

3 April 1997 Comparison of class-selective rejection rules for OCR

Thien M. Ha

Proceedings Volume 3027, Document Recognition IV; (1997) https://doi.org/10.1117/12.270069
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

A rejection rule is called class-selective if it does not reject an ambiguous pattern from all classes but only from those classes that are most unlikely to issue the pattern. A class-selective rejection rule makes a correct decision if the true class of the pattern is among the selected classes; otherwise, i.e., the true class is rejected, it commits an error. The risk of making an error can be reduced by increasing the number of selected classes. Thus the power of a class-selective rejection rule is characterized by the tradeoff between the error rate and the average number of selected classes. Many class-selective rejection rules have been proposed in literature, but the optimal rule was discovered only recently. The rule is optimal in the sense that, for any given average number of classes, it minimizes the error rate, and vice versa. The optimal rule consists in selecting all classes whose posterior probability exceeds a prespecified threshold; if there exist no such classes, the rule simply selects the (a) best class. This paper presents an experimental comparison of the optimal class-selective rejection rule and two other heuristic rules. The experiments are performed on isolated handwritten numerals from the NIST databases. In particular, the tradeoff powers of the three rules are compared using a neutral network based classifier as estimator of posterior probabilities. The experiments show that the theoretically optimal rule does outperform the heuristic rules in practice.

Citation Download Citation

Thien M. Ha "Comparison of class-selective rejection rules for OCR", Proc. SPIE 3027, Document Recognition IV, (3 April 1997); https://doi.org/10.1117/12.270069

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KEYWORDS

Databases

Neural networks

Optical character recognition

Binary data

Error analysis

Feature extraction

Pattern recognition

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