Online Handwritten Cursive Word Recognition Using Segmentation-Free MRF in Combination with P2DBMN-MQDF

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Abstract
This paper describes an online handwritten English cursive word recognition method using a segmentation-free Markov random field (MRF) model in combination with an offline recognition method which uses pseudo 2D bi-moment normalization (P2DBMN) and modified quadratic discriminant function (MQDF). It extracts feature points along the pen-tip trace from pen-down to pen-up and uses the feature point coordinates as unary features and the differences in coordinates between the neighboring feature points as binary features. Each character is modeled as a MRF and word MRFs are constructed by concatenating character MRFs according to a trie lexicon of words during recognition. Our method expands the search space using a character-synchronous beam search strategy to search the segmentation and recognition paths. This method restricts the search paths from the trie lexicon of words and preceding paths, as well as the lengths of feature points during path search. We also combine it with a P2DBMN-MQDF recognizer that is widely used for Chinese and Japanese character recognition.

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