Currently, most top-performing text detection networks tend to employ fixed-size anchor boxes to guide the search for text instances. ey usually rely on a large amount of anchors with different scales to discover texts in scene images, thus leading to high computational cost. In this paper, we propose an end-to-end boxbased text detector with scale-adaptive anchors, which can dynamically adjust the scales of anchors according to the sizes of underlying texts by introducing an additional scale regression layer. e proposed scale-adaptive anchors allow us to use a few number of anchors to handle multi-scale texts and therefore significantly improve the computational efficiency. Moreover, compared to discrete scales used in previous methods, the learned continuous scales are more reliable, especially for small texts detection. Additionally, we propose Anchor convolution to be er exploit necessary feature information by dynamically adjusting the sizes of receptive fields according to the learned scales. Extensive experiments demonstrate that the proposed detector is fast, taking only 0.28 second per image, while outperforming most state-of-the-art methods in accuracy.
Yuan et al. REF learns scale-adaptive anchors to better handle multi-scale text using fewer anchors.
A Single Shot Text Detector with Scale-adaptive Anchors
ArXiv
ArXiv
Computer Science