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.
@article{arxiv.1805.11161,
title = {Confidence Prediction for Lexicon-Free OCR},
author = {Noam Mor and Lior Wolf},
journal= {arXiv preprint arXiv:1805.11161},
year = {2018}
}