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Complementary algorithms for the recognition of totally unconstrained handwritten numerals

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3 Author(s)
Nadal, C. ; Concordia Univ., Montreal, Que., Canada ; Legault, R. ; Suen, C.Y.

Two novel methods for recognizing totally unconstrained handwritten numerals are presented. One classifies samples based on structural features extracted from their skeletons; the other makes use of their contours. Both methods achieve high recognition rates (86.05%, 93.90%) and low substitution rates (2.25%, 1.60%). To take advantage of the inherent complementarity of the two methods, different ways of combining them are studied. It is shown that it is possible to reduce the substitution rate to 0.70%, while the recognition rate remains as high as 92.00% . Furthermore, if reliability is of utmost importance, one can avoid substitutions completely (reliability 100%) and still retain a fairly high recognition rate (84.85%)

Published in:

Pattern Recognition, 1990. Proceedings., 10th International Conference on  (Volume:i )

Date of Conference:

16-21 Jun 1990