E2E-MLT - An Unconstrained End-to-End Method for Multi-language Scene Text
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00337397" target="_blank" >RIV/68407700:21230/19:00337397 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-319-10602-1_26" target="_blank" >https://doi.org/10.1007/978-3-319-10602-1_26</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-21074-8_11" target="_blank" >10.1007/978-3-030-21074-8_11</a>
Alternative languages
Result language
angličtina
Original language name
E2E-MLT - An Unconstrained End-to-End Method for Multi-language Scene Text
Original language description
An end-to-end trainable (fully differentiable) method for multi-language scene text localization and recognition is proposed. The approach is based on a single fully convolutional network (FCN) with shared layers for both tasks. E2E-MLT is the first published multi-language OCR for scene text. While trained in multi-language setup, E2E-MLT demonstrates competitive performance when compared to other methods trained for English scene text alone. The experiments show that obtaining accurate multi-language multi-script annotations is a challenging problem. Code and trained models are released publicly at https://github.com/MichalBusta/E2E-MLT.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
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/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ACCVW 2018: Proceedings of the 14th Asian Conference on Computer Vision Workshops
ISBN
978-3-030-21073-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
17
Pages from-to
127-143
Publisher name
Springer
Place of publication
Cham
Event location
Perth
Event date
Dec 4, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000492907100011