PT Unknown
AU Nibal Nayef
   Yash Patel
   Michal Busta
   Pinaki Nath Chowdhury
   Dimosthenis Karatzas
   Wafa Khlif
   Jiri Matas
   Umapada Pal
   Jean-Christophe Burie
   Cheng-lin Liu
   Jean-Marc Ogier
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
PY 2019
BP 1582
EP 1587
DI 10.1109/ICDAR.2019.00254
AB 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.
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