OCR error correction using correction patterns and self-organizing migrating algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247265" target="_blank" >RIV/61989100:27240/21:10247265 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/content/pdf/10.1007/s10044-020-00936-y.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007/s10044-020-00936-y.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10044-020-00936-y" target="_blank" >10.1007/s10044-020-00936-y</a>

Result language

angličtina

Original language name

OCR error correction using correction patterns and self-organizing migrating algorithm

Original language description

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the errors. In this paper, we present an automatic model consisting of both error detection and error correction phases for OCR post-processing. We propose a novel approach of OCR post-processing error correction using correction pattern edits and evolutionary algorithm which has been mainly used for solving optimization problems. Our model adopts a variant of the self-organizing migrating algorithm along with a fitness function based on modifications of important linguistic features. We illustrate how to construct the table of correction pattern edits involving all types of edit operations and being directly learned from the training dataset. Through efficient settings of the algorithm parameters, our model can be performed with high-quality candidate generation and error correction. The experimental results show that our proposed approach outperforms various baseline approaches as evaluated on the benchmark dataset of ICDAR 2017 Post-OCR text correction competition. (C) 2020, Springer-Verlag London Ltd., part of Springer Nature.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Pattern Analysis and Applications

ISSN

1433-7541

e-ISSN

1433-755X

Volume of the periodical

24

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

701-721

UT code for WoS article

000591971700001

EID of the result in the Scopus database

2-s2.0-85096431401