Scene text detection remains a grand challenge due to the variation in text
curvatures, orientations, and aspect ratios. One of the hardest problems in
this task is how to represent text instances of arbitrary shapes. Although many
methods have been proposed to model irregular texts in a flexible manner, most
of them lose simplicity and robustness. Their complicated post-processings and
the regression under Dirac delta distribution undermine the detection
performance and the generalization ability. In this paper, we propose an
efficient text instance representation named CentripetalText (CT), which
decomposes text instances into the combination of text kernels and centripetal
shifts. Specifically, we utilize the centripetal shifts to implement pixel
aggregation, guiding the external text pixels to the internal text kernels. The
relaxation operation is integrated into the dense regression for centripetal
shifts, allowing the correct prediction in a range instead of a specific value.
The convenient reconstruction of text contours and the tolerance of prediction
errors in our method guarantee the high detection accuracy and the fast
inference speed, respectively. Besides, we shrink our text detector into a
proposal generation module, namely CentripetalText Proposal Network, replacing
Segmentation Proposal Network in Mask TextSpotter v3 and producing more
accurate proposals. To validate the effectiveness of our method, we conduct
experiments on several commonly used scene text benchmarks, including both
curved and multi-oriented text datasets. For the task of scene text detection,
our approach achieves superior or competitive performance compared to other
existing methods, e.g., F-measure of 86.3% at 40.0 FPS on Total-Text, F-measure
of 86.1% at 34.8 FPS on MSRA-TD500, etc. For the task of end-to-end scene text
recognition, our method outperforms Mask TextSpotter v3 by 1.1% on Total-Text.

提出一种名为 CentripetalText 的文本实例表示方法，通过将文本实例分解为文本核和向心移位的组合来提高检测精度，在检测和识别场景文本方面表现良好。

CentripetalText: 一种用于场景文本检测的高效文本实例表示

CentripetalText: An Efficient Text Instance Representation for Scene  Text Detection

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。