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Title: Towards robust curve text detection with conditional spatial expansion
Authors: Liu, Zichuan
Lin, Guosheng
Yang, Sheng
Liu, Fayao
Lin, Weisi
Goh, Wang Ling
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Liu, Z., Lin, G., Yang, S., Liu, F., Lin, W., & Goh, W. L. (2019). Towards robust curve text detection with conditional spatial expansion. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/CVPR.2019.00744
Project: AISG-RP-2018-003 
[RG126/17 (S) 
Abstract: It is challenging to detect curve texts due to their irregular shapes and varying sizes. In this paper, we first investigate the deficiency of the existing curve detection methods and then propose a novel Conditional Spatial Expansion (CSE) mechanism to improve the performance of curve text detection. Instead of regarding the curve text detection as a polygon regression or a segmentation problem, we treat it as a region expansion process. Our CSE starts with a seed arbitrarily initialized within a text region and progressively merges neighborhood regions based on the extracted local features by a CNN and contextual information of merged regions. The CSE is highly parameterized and can be seamlessly integrated into existing object detection frameworks. Enhanced by the data-dependent CSE mechanism, our curve text detection system provides robust instance-level text region extraction with minimal post-processing. The analysis experiment shows that our CSE can handle texts with various shapes, sizes, and orientations, and can effectively suppress the false-positives coming from text-like textures or unexpected texts included in the same RoI. Compared with the existing curve text detection algorithms, our method is more robust and enjoys a simpler processing flow. It also creates a new state-of-art performance on curve text benchmarks with Fscore of up to 78.4%.
DOI: 10.1109/CVPR.2019.00744
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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