State-of-the-art scene text detection techniques predict quadrilateral boxes
that are prone to localization errors while dealing with straight or curved
text lines of different orientations and lengths in scenes. This paper presents
a novel multi-scale shape regression network (MSR) that is capable of locating
text lines of different lengths, shapes and curvatures in scenes. The proposed
MSR detects scene texts by predicting dense text boundary points that
inherently capture the location and shape of text lines accurately and are also
more tolerant to the variation of text line length as compared with the state
of the arts using proposals or segmentation. Additionally, the multi-scale
network extracts and fuses features at different scales which demonstrates
superb tolerance to the text scale variation. Extensive experiments over
several public datasets show that the proposed MSR obtains superior detection
performance for both curved and straight text lines of different lengths and
orientations.

本文提出了一种新的多尺度形状回归网络（MSR），能够在场景中定位不同长度、形状和曲率的文本行，并通过预测密集的文本边界点来探测场景文本，具有较高的文本行长度变化容忍度。此外，该多尺度网络对不同尺度的特征提取及融合表现出优异的容忍度，实验结果显示，在曲线和直线不同长度及取向的场景中，所提出的 MSR 算法具备优越的探测性能。