ICDAR2019 Robust Reading Challenge onMulti-lingual Scene Text Detection and Recognition– RRC-MLT-2019
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00337615" target="_blank" >RIV/68407700:21230/19:00337615 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICDAR.2019.00254" target="_blank" >http://dx.doi.org/10.1109/ICDAR.2019.00254</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDAR.2019.00254" target="_blank" >10.1109/ICDAR.2019.00254</a>
Alternative languages
Result language
angličtina
Original language name
ICDAR2019 Robust Reading Challenge onMulti-lingual Scene Text Detection and Recognition– RRC-MLT-2019
Original language description
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense.With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of theRRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method.The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.
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/TE01020415" target="_blank" >TE01020415: V3C - Visual Computing Competence Center</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
ICDAR2019: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition
ISBN
978-1-7281-3015-6
ISSN
1520-5363
e-ISSN
2379-2140
Number of pages
6
Pages from-to
1582-1587
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Sydney
Event date
Sep 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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