Off-line Handwriting Recognition
نویسندگان
چکیده
abstract In this paper, we present a new approach to unconstrained, oo-line handwriting recognition (hwr). It is based on the global plausibility estimation of a word knowing the local probability of each individual character. Having a set of character models issued from a training step and an input word pattern, we maximize for each word of the lexicon the sum of plausibilities of its component characters. This maximisation is made in terms of observation quality and extent of a symbol within the pattern. The proposed method operates in a top-down manner by giving segmentation hypotheses which induce local symbol extents for a given word of the lexicon against which the pattern is matched. The word which obtains the highest average plausibility per character is labeled as the recognized one. This method was applied with success on continuous speech recognition by 11].
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