Learning Shape-Aware Embedding for Scene Text Detection

Zhuotao Tian, Michelle Shu, Pengyuan Lyu, Ruiyu Li, Chao Zhou, Xiaoyong Shen, Jiaya Jia; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4234-4243

Abstract


We address the problem of detecting scene text in arbitrary shapes, which is a challenging task due to the high variety and complexity of the scene. Specifically, we treat text detection as instance segmentation and propose a segmentation-based framework, which extracts each text instance as an independent connected component. To distinguish different text instances, our method maps pixels onto an embedding space where pixels belonging to the same text are encouraged to appear closer to each other and vise versa. In addition, we introduce a Shape-Aware Loss to make training adaptively accommodate various aspect ratios of text instances and the tiny gaps among them, and a new post-processing pipeline to yield precise bounding box predictions. Experimental results on three challenging datasets (ICDAR15, MSRA-TD500 and CTW1500) demonstrate the effectiveness of our work.

Related Material


[pdf]
[bibtex]
@InProceedings{Tian_2019_CVPR,
author = {Tian, Zhuotao and Shu, Michelle and Lyu, Pengyuan and Li, Ruiyu and Zhou, Chao and Shen, Xiaoyong and Jia, Jiaya},
title = {Learning Shape-Aware Embedding for Scene Text Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}