Reading arbitrary-shaped text in an end-to-end fashion has received particularly growing interested in computer vision. In this paper, we study the problem of scene text spotting, which aims to detect and recognize text from cluttered images simultaneously and propose an end-to-end trainable neural network named Boundary TextSpotter. Different from existing methods that describe the shape of text instance with bounding box or shape mask, Boundary TextSpotter formulates it as a set of boundary points. Besides, the representation of such boundary points provides the order of reading text. Benefiting from the representation on both detection and recognition, Boundary TextSpotter can easily deal with the text of arbitrary shapes. Further, to efficiently detect the boundary points of the text, a single-stage text detector is proposed, which can almost perform at a real-time speed. Experiments on three challenging datasets, including ICDAR2015, Total-Text and CTW1500 demonstrate that the proposed method achieves state-of-the-art or competitive results, meanwhile significantly improving the inference speed.

中文翻译：

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边界文本定位器：走向任意形状的场景文本定位

以端到端的方式阅读任意形状的文本已引起人们对计算机视觉的兴趣日益浓厚。在本文中，我们研究了场景文本定位问题，旨在同时检测和识别杂乱图像中的文本，并提出了一种名为 Boundary TextSpotter 的端到端可训练神经网络。与使用边界框或形状掩码描述文本实例形状的现有方法不同，Boundary TextSpotter 将其表述为一组边界点。此外，这些边界点的表示提供了阅读文本的顺序。受益于检测和识别的表示，Boundary TextSpotter 可以轻松处理任意形状的文本。此外，为了有效地检测文本的边界点，提出了一种单级文本检测器，几乎可以实时执行。在 ICDAR2015、Total-Text 和 CTW1500 三个具有挑战性的数据集上进行的实验表明，所提出的方法实现了最先进或具有竞争力的结果，同时显着提高了推理速度。