Semi-automatic Handwritten Word Segmentation Based on Character Width Approximation Via Maximum Likelihood Method and Regression Model
The paper presents a method of word image segmentation into images of individual characters. The method is semi-automatic, because it requires that the character sequence constituting the word on the image is know. It is assumed that widths of the characters in the alphabet are random variables and that the parametres of probability distribution are specific for each character. At the first stage of the proposed method the parameters of the distributions for all alphabet characters are estimated. Then for each word in the corpus being processed all possible segmentation variants are analyzed and for each variant its probability is calculated taking into account probability distrubution of corresponding characters. Finally, such segmentation variant is selected for which the calculated probability is highest.
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