  Scene text detection is an important step of scene text reading system. The
main challenges lie on significantly varied sizes and aspect ratios, arbitrary
orientations and shapes. Driven by recent progress in deep learning, impressive
performances have been achieved for multi-oriented text detection. Yet, the
performance drops dramatically in detecting curved texts due to the limited
text representation (e.g., horizontal bounding boxes, rotated rectangles, or
quadrilaterals). It is of great interest to detect curved texts, which are
actually very common in natural scenes. In this paper, we present a novel text
detector named TextField for detecting irregular scene texts. Specifically, we
learn a direction field pointing away from the nearest text boundary to each
text point. This direction field is represented by an image of two-dimensional
vectors and learned via a fully convolutional neural network. It encodes both
binary text mask and direction information used to separate adjacent text
instances, which is challenging for classical segmentation-based approaches.
Based on the learned direction field, we apply a simple yet effective
morphological-based post-processing to achieve the final detection.
Experimental results show that the proposed TextField outperforms the
state-of-the-art methods by a large margin (28% and 8%) on two curved text
datasets: Total-Text and CTW1500, respectively, and also achieves very
competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500.
Furthermore, TextField is robust in generalizing to unseen datasets. The code
is available at https://github.com/YukangWang/TextField.
