Multi-scale Scene Text Detection via Resolution Transform
Peirui Cheng, Weiqiang Wang, Yuanqiang Cai · 2019
Scene text detection is a challenging task because there are many small text targets in the natural scene and the size of scene text varies greatly. For the current popular scene text detection methods, such as EAST and Textboxes, it is difficult to detect small text and text with large differences in size well at the same time. To solve the problem, we propose a novel multi-scale scene text detection method based on EAST. The proposed method extracts high-resolution feature maps at multiple scales via resolution transform and detects text on these feature maps. Through the resolution transform module, the proposed method can detect both kinds of text well. Besides, we use aggregated feature pyramid module to efficiently pass both low-level and high-level information to feature maps at each scale. Experiments on datasets ICDAR2015 and COCO-Text demonstrate that the proposed method has a comparable performance with state-of-the-art methods and it is more efficient. For ICDAR2015 and COCO-Text datasets, the proposed method achieves an F-score of 0.84 and 0.42 respectively.