Journal
PATTERN RECOGNITION
Volume 110, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2020.107336
Keywords
Scene text detection; Curved text; Multi-oriented text; CNN; Deep learning
Funding
- National Key R&D Program of China [2017YFB1002202]
- National Natural Science Foundation of China [61671422, U1613211]
- Key Science and Technology Project of Anhui Province [17030901005]
- MOE-Microsoft Key Laboratory of University of Science and Technology of China
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This paper introduces a novel scene text detection method named TextMountain, which utilizes border-center information and can effectively handle multi-oriented and curved text. Experimental results demonstrate better performance in terms of both accuracy and efficiency.
In this paper, we propose a novel scene text detection method named TextMountain. The key idea of TextMountain is making full use of border-center information. Different from previous works that treat center-border as a binary classification problem, we predict text center-border probability (TCBP) and text center-direction (TCD). The TCBP is just like a mountain whose top is text center and foot is text border. The mountaintop can separate text instances which cannot be easily achieved using semantic segmentation map and its rising direction can plan a road to top for each pixel on mountain foot at the group stage. The TCD helps TCBP learning better. Our label rules will not lead to the ambiguous problem with the transformation of angle, so the proposed method is robust to multi-oriented text and can also handle well curved text. In inference stage, each pixel at the mountain foot needs to search the path to the mountaintop and this process can be efficiently completed in parallel, yielding the efficiency of our method compared with others. The experiments on MLT, ICDAR2015, RCTW-17 and SCUT-CTW150 0 datasets demonstrate that the proposed method achieves better or comparable performance in terms of both accuracy and efficiency. It is worth mentioning our method achieves an F-measure of 76.85% on MLT which outperforms the previous methods by a large margin. Code will be made available. (c) 2020 Elsevier Ltd. All rights reserved.
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