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Translation-Inspired OCR

Dmitriy Genzel
Nemanja Spasojevic
Michael Jahr
Frank Yung-Fong Tang


Optical character recognition is carried out using techniques borrowed from statistical machine translation. In particular, the use of multiple simple feature functions in linear combination, along with minimum-error-rate training, integrated decoding, and $N$-gram language modeling is found to be remarkably effective, across several scripts and languages. Results are presented using both synthetic and real data in five languages.

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