&lt;title&gt;Cursive script segmentation and recognition by dynamic programming&lt;/title&gt;

Man, G.M.T.; Poon, J.C.H.

doi:10.1117/12.143620

Cited by 2 publications

(1 citation statement)

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“…However, we found only two methods of this kind in the literature. Shridhar et al, [75] and Man et al, [58], did not constrain the k th character to be a specific one but chose the most likely in the current block of primitive segments. In this case, the evaluation function (match) may be replaced by an ordinary OCR module that outputs the most likely character and its confidence given a partition of the word image.…”

Section: Dur(lex Entry[j]) If (Num Seg Of Word > Max Seg) Rejectmentioning

confidence: 99%

Offline cursive script word recognition ? a survey

Steinherz

Rivlin

Intrator

1999

International Journal on Document Analysis and Recognition

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Abstract.We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to bootstrap a few candidates for a final evaluation phase.

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Section: Dur(lex Entry[j]) If (Num Seg Of Word > Max Seg) Rejectmentioning

confidence: 99%