  Having a reliable accuracy score is crucial for real world applications of
OCR, since such systems are judged by the number of false readings.
Lexicon-based OCR systems, which deal with what is essentially a multi-class
classification problem, often employ methods explicitly taking into account the
lexicon, in order to improve accuracy. However, in lexicon-free scenarios,
filtering errors requires an explicit confidence calculation. In this work we
show two explicit confidence measurement techniques, and show that they are
able to achieve a significant reduction in misreads on both standard benchmarks
and a proprietary dataset.

Computer Vision and Pattern Recognition