Single Shot Text Detector with Regional Attention

ICCV 2017  ·  Pan He, Weilin Huang, Tong He, Qile Zhu, Yu Qiao, Xiaolin Li ·

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN- based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allow- ing the detector to work reliably on multi-scale and multi- orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench- mark, advancing the state-of-the-art results in [18, 28]. Demo is available at:

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Scene Text Detection COCO-Text SSTD F-Measure 37 # 4
Precision 46 # 4
Recall 31 # 4
Scene Text Detection ICDAR 2013 SSTD F-Measure 87% # 10
Precision 88 # 12
Recall 86 # 7
Scene Text Detection ICDAR 2015 EAST + PVANET2x RBOX (multi-scale) F-Measure 80.7 # 34
Precision 83.3 # 37
Recall 78.3 # 34