Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315723" target="_blank" >RIV/68407700:21230/17:00315723 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICCV.2017.242" target="_blank" >http://dx.doi.org/10.1109/ICCV.2017.242</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCV.2017.242" target="_blank" >10.1109/ICCV.2017.242</a>
Alternative languages
Result language
angličtina
Original language name
Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework
Original language description
A method for scene text localization and recognition is proposed. The novelties include: training of both text detection and recognition in a single end-to-end pass, the structure of the recognition CNN and the geometry of its input layer that preserves the aspect of the text and adapts its resolution to the data. The proposed method achieves state-of-the-art accuracy in the end-to-end text recognition on two standard datasets -- ICDAR 2013 and ICDAR 2015, whilst being an order of magnitude faster than competing methods - the whole pipeline runs at $10$ frames per second on an NVidia K80 GPU.
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
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)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2017 IEEE International Conference on Computer Vision (ICCV 2017)
ISBN
978-1-5386-1032-9
ISSN
1550-5499
e-ISSN
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Number of pages
9
Pages from-to
2223-2231
Publisher name
IEEE
Place of publication
Piscataway
Event location
Venice
Event date
Oct 22, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000425498402030