An In-depth Analysis of OCR Errors for Unconstrained Vietnamese Handwriting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10247263" target="_blank" >RIV/61989100:27240/20:10247263 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/20:10247263

Výsledek na webu

<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-63924-2_26.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-3-030-63924-2_26.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-63924-2_26" target="_blank" >10.1007/978-3-030-63924-2_26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An In-depth Analysis of OCR Errors for Unconstrained Vietnamese Handwriting

Popis výsledku v původním jazyce

OCR post-processing is an essential step to improve the accuracy of OCR-generated texts by detecting and correcting OCR errors. In this paper, the OCR texts are resulted from an OCR engine which is based on the attention-based encoder-decoder model for unconstrained Vietnamese handwriting. We identify various kinds of Vietnamese OCR errors and their possible causes. Detailed statistics of Vietnamese OCR errors are provided and analyzed at both character level and syllable level, using typical OCR error characteristics such as error rate, error mapping/edit, frequency and error length. Furthermore, the statistical analyses are done on training and test sets of a benchmark database to infer whether the test set is the appropriate representative of the training set regarding the OCR error characteristics. We also discuss the choice of designing OCR post-processing approaches at character level or at syllable level relying on provided statistics of studied datasets. (C) 2020, Springer Nature Switzerland AG.

Název v anglickém jazyce

An In-depth Analysis of OCR Errors for Unconstrained Vietnamese Handwriting

Popis výsledku anglicky

OCR post-processing is an essential step to improve the accuracy of OCR-generated texts by detecting and correcting OCR errors. In this paper, the OCR texts are resulted from an OCR engine which is based on the attention-based encoder-decoder model for unconstrained Vietnamese handwriting. We identify various kinds of Vietnamese OCR errors and their possible causes. Detailed statistics of Vietnamese OCR errors are provided and analyzed at both character level and syllable level, using typical OCR error characteristics such as error rate, error mapping/edit, frequency and error length. Furthermore, the statistical analyses are done on training and test sets of a benchmark database to infer whether the test set is the appropriate representative of the training set regarding the OCR error characteristics. We also discuss the choice of designing OCR post-processing approaches at character level or at syllable level relying on provided statistics of studied datasets. (C) 2020, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 12466

ISBN

978-3-030-63923-5

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

14

Strana od-do

448-461

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Quy Nhon

Datum konání akce

25. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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