ICDAR2019 Robust Reading Challenge onMulti-lingual Scene Text Detection and Recognition– RRC-MLT-2019

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

ICDAR2019 Robust Reading Challenge onMulti-lingual Scene Text Detection and Recognition– RRC-MLT-2019

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

ICDAR2019 Robust Reading Challenge onMulti-lingual Scene Text Detection and Recognition– RRC-MLT-2019

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/TE01020415" target="_blank" >TE01020415: Centrum kompetence ve zpracování vizuálních informací (V3C - Visual Computing Competence Center)</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

Počet stran výsledku

6

Strana od-do

1582-1587

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Sydney

Datum konání akce

20. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—