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Text detection is the premise and guarantee of text recognition. Multi-oriented text detection is the current research hotspot. Due to the variability in size, spatial layout, color and the arrangement direction of natural scene text, natural scene text detection is still very challenging. Therefore, this paper proposes a simple and fast multi-oriented text detection method. Our method first optimizes the regression branch by designing a diagonal adjustment factor to make the position regression more accurate, which increases F-score by 0.8. Secondly, we add an attention module to the model, which improves the accuracy of detecting small text regions and increases F-score by 1.2. Then, we introduce DR Loss to solve the problem of positive and negative sample imbalance, which increases F-score by 0.5. Finally, we conduct experimental verification and analysis on the ICDAR2015, MSRA-TD500 and ICDAR2013 datasets. The experimental results demonstrate that this method can significantly improve the precision and recall of scene text detection, and it has achieved competitive results compared with existing advanced methods. On the ICDAR 2015 dataset, the proposed method achieves an F-score of 0.849 at 9.9fps at 720p resolution. On the MSRA-TD500 dataset, the proposed method achieves an F-score of 0.772 at 720p resolution. On the ICDAR 2013 dataset, the proposed method achieves an F-score of 0.887 at 720p resolution.

FDTA: Fully Convolutional Scene Text Detection With Text Attention