FEDS -- Filtered Edit Distance Surrogate
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351133" target="_blank" >RIV/68407700:21230/21:00351133 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-86337-1_12" target="_blank" >https://doi.org/10.1007/978-3-030-86337-1_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-86337-1_12" target="_blank" >10.1007/978-3-030-86337-1_12</a>
Alternative languages
Result language
angličtina
Original language name
FEDS -- Filtered Edit Distance Surrogate
Original language description
This paper proposes a procedure to train a scene text recognition model using a robust learned surrogate of edit distance. The proposed method borrows from self-paced learning and filters out the training examples that are hard for the surrogate. The filtering is performed by judging the quality of the approximation, using a ramp function, enabling end-to-end training. Following the literature, the experiments are conducted in a post-tuning setup, where a trained scene text recognition model is tuned using the learned surrogate of edit distance. The efficacy is demonstrated by improvements on various challenging scene text datasets such as IIIT-5K, SVT, ICDAR, SVTP, and CUTE. The proposed method provides an average improvement of 11.2% on total edit distance and an error reduction of 9.5% on accuracy.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICDAR2021: 16th IAPR International Conference on Document Analysis and Recognition
ISBN
978-3-030-86336-4
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
16
Pages from-to
171-186
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Lausanne
Event date
Sep 5, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000711880100012