BOTD: Bold Outline Text Detector

C. Yang, Z. Xiong, M. Chen, Q. Wang, X. Li

Recently, text detection for arbitrary shape has attracted more and more search attention. Although segmentation-based methods, which are not limited by the text shape, have been studied to improve the performance, the slow detection speed, complicated post-processing, and text adhesion problem are still limitations for the practical application. In this paper, we propose a simple yet effective arbitrary-shape text detector, named Bold Outline Text Detector (BOTD). It is a novel one-stage detection framework with few post-processing processes. At the same time, the text adhesion problem can also be well alleviated. Specifically, BOTD first generates a center mask (CM) for each text instance, which makes the adhesive text easy to distinguish. Base on the CM, we further compute the polar minimum distance (PMD) for each text instance. PMD denotes the shortest distance between the center point of CM and the outline of the text instance. By dividing the text mask into CM and PMD, the outline of arbitrary-shape text instance can be obtained by simply predicting its CM and PMD. Without any bells and whistles, BOTD achieves an F-measure of 80.1% on CTW1500 with 52 FPS. Note that the post-processing time only accounts for 9% of the whole inference time. Code and trained models will be publicly available soon.

Knowledge Graph



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