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.