Shape Robust Text Detection with Progressive Scale Expansion Network

7 Jun 2018  ·  Xiang Li, Wenhai Wang, Wenbo Hou, Ruo-Ze Liu, Tong Lu, Jian Yang ·

The challenges of shape robust text detection lie in two aspects: 1) most existing quadrangular bounding box based detectors are difficult to locate texts with arbitrary shapes, which are hard to be enclosed perfectly in a rectangle; 2) most pixel-wise segmentation-based detectors may not separate the text instances that are very close to each other. To address these problems, we propose a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance... These predictions correspond to different `kernels' produced by shrinking the original text instance into various scales. Consequently, the final detection can be conducted through our progressive scale expansion algorithm which gradually expands the kernels with minimal scales to the text instances with maximal and complete shapes. Due to the fact that there are large geometrical margins among these minimal kernels, our method is effective to distinguish the adjacent text instances and is robust to arbitrary shapes. The state-of-the-art results on ICDAR 2015 and ICDAR 2017 MLT benchmarks further confirm the great effectiveness of PSENet. Notably, PSENet outperforms the previous best record by absolute 6.37\% on the curve text dataset SCUT-CTW1500. Code will be available in read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Scene Text Detection ICDAR 2015 PSENet-1s F-Measure 87.1 # 15
Precision 88.7 # 18
Recall 85.5 # 14
Scene Text Detection ICDAR 2017 MLT PSENet-1s Precision 77.01 # 12
Recall 68.4 # 10
F-Measure 72.45% # 8
Scene Text Detection SCUT-CTW1500 PSENet-1s F-Measure 81.17 # 6
Precision 82.5 # 6
Recall 79.89 # 6


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