An extension of a fuzzy substroke extractor

In employing a structural approach to recognize handwritten Chinese characters (HCC), substroke is usually chosen as a feature set. Various types of ambiguities may arise during the substroke extraction. A fuzzy substroke extractor was previously (1990) proposed by the authors to handle a number of ambiguities caused by substroke touchings. The extractor then made use of the scoring information on the extracted items to produce a set of “consistent” outputs, from which no edge-sharing is found among the extracted items. The major advantage gained from this approach is that it is possible to reconstruct the skeleton in a “just-fit” mode. Thus, for the candidate set with most of the desirable extracted substrokes, the noise rate is much lower than those reported by using a conventional “explore-all-possibility” approach. To achieve this goal, the segmentation of character skeleton is transformed into a fuzzy set partitioning task. This paper extends our current technique to handle another type of ambiguity problem in substroke extraction, i.e. broken substrokes. Two cases of broken substrokes are addressed. Under certain conditions, virtual edges are introduced to “complete” the “supposed” broken skeleton graph. By considering this new skeleton as the actual input, suspected broken substrokes are detectable as well. With this proposed extension, most ambiguities encountered during substroke extraction can now be successfully treated in a unified framework