Abstract
In this paper the design characteristics of a recognition system for unconstrained handprinted symbols are described. The targeted symbols and accuracy requirements from a mapping and charting environment posed problems requiring the development of new OCR techniques. The recognition system employs smart thinning algorithms to produce centerline thinned stick figure images from raster scanned characters. The recognition logic interacts with the feature extraction algorithms to extract just those topological, geometrical and local measurements that are needed to identify the character or to reject the character as unrecognizable. Results from a numeric character data base consist of some 7000 numerals collected from a broad spectrum of sources and yield an efficiency rate of 97%. Substitution error rate is 0.3% and rejection rate is 2.7%. Preliminary results for the recognition of alphabetic characters are also discussed.
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