Confidence Prediction for Lexicon-Free OCR

Noam Mor, Lior Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-225
Number of pages8
ISBN (Electronic)9781538648865
DOIs
StatePublished - 3 May 2018
Event18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States
Duration: 12 Mar 201815 Mar 2018

Publication series

NameProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Volume2018-January

Conference

Conference18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Country/TerritoryUnited States
CityLake Tahoe
Period12/03/1815/03/18

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