TY - GEN
T1 - Confidence Prediction for Lexicon-Free OCR
AU - Mor, Noam
AU - Wolf, Lior
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/5/3
Y1 - 2018/5/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85050944004&partnerID=8YFLogxK
U2 - 10.1109/WACV.2018.00030
DO - 10.1109/WACV.2018.00030
M3 - منشور من مؤتمر
T3 - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
SP - 218
EP - 225
BT - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Y2 - 12 March 2018 through 15 March 2018
ER -