Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes

By Chengquan Zhang, Borong Liang, Zuming Huang, Mengyi En, Junyu Han, Errui Ding, Xinghao Ding. 2019

256 citations recorded by the source corpus.

Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectangle bounding box or quadrangle adopted to describe text, previous methods may fall short when dealing with more challenging text instances, such as extremely long text and arbitrarily shaped text. To address these two problems, we present a novel text detector namely LOMO, which localizes the text progressively for multiple times (or in other word, LOok More than Once). LOMO consists of a direct regressor (DR), an iterative refinement module (IRM) and a shape expression module (SEM). At first, text proposals in the form of quadrangle are generated by DR branch. Next, IRM progressively perceives the entire long text by iterative refinement based on the extracted feature blocks of preliminary proposals. Finally, a SEM is introduced to reconstruct more precise representation of irregular text by considering the geometry properties of text instance, including text region, text center line and border offsets. The state-of-the-art results on several public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015 and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xue Yang
2. Chunhua Shen
3. Hao Chen
4. Tong He
5. Lianwen Jin
6. Yuliang Liu
7. Xiang Bai
8. Junchi Yan

In Media Technology

1. Xue Yang
2. Wen Qian
3. Lianwen Jin
4. Silong Peng
5. Chunhua Shen
6. Hao Chen
7. Yuliang Liu
8. Tong He

Citations in the corpus

• Real-time Scene Text Detection with Differentiable Binarization (2019, unknown): “Net. Method P R F FPS TextSnake (Long et al. 2018) 82.7 74.5 78.4 - ATRR (Wang et al. 2019b) 80.9 76.2 78.5 - MTS (Lyu et al. 2018a) 82.5 75.6 78.6 - TextField (Xu et al. 2019) 81.2 79.9 80.6 - LOMO (Zhang et al. 2019)* 87.6 79.3 83.3 - CRAFT (Baek et al. 2019) 87.6 79.9 83.6 - CSE (Liu et al. 2019b”
• Learning Modulated Loss for Rotated Object Detection (2019, methodology): “And then we resort to the eight-parameter regression model [22, 23, 24, 25] (As will be shown later in the paper, the discontinuity caused by rotation boundary inherently exist in eight-parameter system) to avoid regression parameter inconsistency.”
• ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network (2020, methodology): “Compared to segmentation-based methods [41, 46, 1, 39, 47, 29], regression-based methods are more direct solutions to oriented or curved text detection, e.”