Trigram-based algorithms for OCR result correction

Konstantin Bulatov; Temudzhin Manzhikov; Oleg Slavin; Igor Faradjev; Igor Janiszewski

doi:10.1117/12.2268559

17 March 2017 Trigram-based algorithms for OCR result correction

Konstantin Bulatov, Temudzhin Manzhikov, Oleg Slavin, Igor Faradjev, Igor Janiszewski

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410O (2017) https://doi.org/10.1117/12.2268559
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

In this paper we consider a task of improving optical character recognition (OCR) results of document fields on low-quality and average-quality images using N-gram models. Cyrillic fields of Russian Federation internal passport are analyzed as an example. Two approaches are presented: the first one is based on hypothesis of dependence of a symbol from two adjacent symbols and the second is based on calculation of marginal distributions and Bayesian networks computation. A comparison of the algorithms and experimental results within a real document OCR system are presented, it's showed that the document field OCR accuracy can be improved by more than 6% for low-quality images.

Citation Download Citation

Konstantin Bulatov, Temudzhin Manzhikov, Oleg Slavin, Igor Faradjev, and Igor Janiszewski "Trigram-based algorithms for OCR result correction", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410O (17 March 2017); https://doi.org/10.1117/12.2268559

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available