Offline Recognition of Large Vocabulary Cursive Handwritten Text

This paper presents a system for the offline recognition of cursive handwritten lines of text. The system is based on continuous density HMMs and Statistical Language Models. The system recognizes data produced by a single writer. No a-priori knowledge is used about the content of the text to be recognized. Changes in the experimental setup with respect to the recognition of single words are highlighted. The results show a recognition rate of 85% with a lexicon containing 50'000 words. The experiments were performed over a publicly available database.

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