CentripetalText: An Efficient Text Instance Representation for Scene Text Detection

• URL: http://arxiv.org/abs/2107.05945v1
• Date: Tue, 13 Jul 2021 09:34:18 GMT
• Title: CentripetalText: An Efficient Text Instance Representation for Scene Text Detection
• Authors: Tao Sheng, Jie Chen, Zhouhui Lian
• Abstract summary: We propose an efficient text instance representation named CentripetalText (CT) CT decomposes text instances into the combination of text kernels and centripetal shifts. For the task of scene text detection, our approach achieves superior or competitive performance compared to other existing methods.
• Score: 19.69057252363207
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scene text detection remains a grand challenge due to the variation in text curvatures, orientations, and aspect ratios. One of the most intractable problems is how to represent text instances of arbitrary shapes. Although many state-of-the-art methods have been proposed to model irregular texts in a flexible manner, most of them lose simplicity and robustness. Their complicated post-processings and the regression under Dirac delta distribution undermine the detection performance and the generalization ability. In this paper, we propose an efficient text instance representation named CentripetalText (CT), which decomposes text instances into the combination of text kernels and centripetal shifts. Specifically, we utilize the centripetal shifts to implement the pixel aggregation, which guide the external text pixels to the internal text kernels. The relaxation operation is integrated into the dense regression for centripetal shifts, allowing the correct prediction in a range, not a specific value. The convenient reconstruction of the text contours and the tolerance of the prediction errors in our method guarantee the high detection accuracy and the fast inference speed respectively. Besides, we shrink our text detector into a proposal generation module, namely CentripetalText Proposal Network (CPN), replacing SPN in Mask TextSpotter v3 and producing more accurate proposals. To validate the effectiveness of our designs, we conduct experiments on several commonly used scene text benchmarks, including both curved and multi-oriented text datasets. For the task of scene text detection, our approach achieves superior or competitive performance compared to other existing methods, e.g., F-measure of 86.3% at 40.0 FPS on Total-Text, F-measure of 86.1% at 34.8 FPS on MSRA-TD500, etc. For the task of end-to-end scene text recognition, we outperform Mask TextSpotter v3 by 1.1% on Total-Text.

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