TY  - CONF
AU  - Nibal Nayef
AU  - Yash Patel
AU  - Michal Busta
AU  - Pinaki Nath Chowdhury
AU  - Dimosthenis Karatzas
AU  - Wafa Khlif
AU  - Jiri Matas
AU  - Umapada Pal
AU  - Jean-Christophe Burie
AU  - Cheng-lin Liu
AU  - Jean-Marc Ogier
A2  - ICDAR
PY  - 2019//
TI  - ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019
BT  - 15th International Conference on Document Analysis and Recognition
SP  - 1582
EP  - 1587
N2  - 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 the RRC-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.
UR  - https://ieeexplore.ieee.org/document/8978096
L1  - http://refbase.cvc.uab.es/files/NPB2019.pdf
UR  - http://dx.doi.org/10.1109/ICDAR.2019.00254
N1  - DAG; 600.121; 600.129
ID  - Nibal Nayef2019
ER  -