AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142899" target="_blank" >RIV/00216305:26230/21:PU142899 - isvavai.cz</a>

Result on the web

<a href="https://pero.fit.vutbr.cz/publications" target="_blank" >https://pero.fit.vutbr.cz/publications</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86337-1_31" target="_blank" >10.1007/978-3-030-86337-1_31</a>

Result language

angličtina

Original language name

AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions

Original language description

This paper addresses text recognition for domains with limited manual annotations by a simple self-training strategy. Our approach should reduce human annotation effort when target domain data is plentiful, such as when transcribing a collection of single person's correspondence or a large manuscript. We propose to train a seed system on large scale data from related domains mixed with available annotated data from the target domain. The seed system transcribes the unannotated data from the target domain which is then used to train a better system. We study several confidence measures and eventually decide to use the posterior probability of a transcription for data selection. Additionally, we propose to augment the data using an aggressive masking scheme. By self-training, we achieve up to 55 % reduction in character error rate for handwritten data and up to 38 % on printed data. The masking augmentation itself reduces the error rate by about 10 % and its effect is better pronounced in case of difficult handwritten data.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Article name in the collection

Lladós J., Lopresti D., Uchida S. (eds) Document Analysis and Recognition - ICDAR 2021

ISBN

978-3-030-86336-4

ISSN

—

e-ISSN

—

Number of pages

14

Pages from-to

463-477

Publisher name

Springer Nature Switzerland AG

Place of publication

Lausanne

Event location

Lausanne, Switzerland

Event date

Sep 5, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000711880100031