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.

本文介绍了基于 RRC-MLT-2017 的文本检测和识别竞赛，该竞赛包含一个新的端到端任务、一个现实图像数据集中的额外语言，一个大规模的多语言合成数据集以辅助训练，并提出了一个基线端到端识别方法。该竞赛总共收到了来自研究和工业界的 60 份提交，本文展示了该竞赛的数据集、任务和发现结果。

ICDAR2019多语言场景文本检测和识别鲁棒性挑战—RRC-MLT-2019