Text detection in complex scene images is a challenging task for intelligent transportation. Recently, anchor mechanisms are widely utilized in scene text detection tasks. However, in existing methods, anchors are generally predefined empirically, degrading robustness to complex scenarios with various sizes and orientation variations. In this paper, we propose a novel Attention Anchor Mechanism (AAM), especially targeting at predicting appropriate anchors for each pixel. To be concrete, we regard a series of predefined anchors as basic anchors and utilize an attention model to predict weights corresponding to basic anchors. Consequently, the weighted sum of basic anchors in each pixel can obtain a predicted anchor. In this way, the gap between the predicted anchors and the corresponding ground truth boxes could be narrowed, making the network easier to regress. For facilitating the design of basic anchors, we adopt a dimension-decomposition mechanism to predict width, height, and angle of anchors, respectively. Extensive experiments on several public datasets demonstrate that our method achieves state-of-the-art performance.

Direct regression and anchor are the two mainly effective and prevailing mechanisms in the paradigm of scene text detection. However, the use of direct regression-based methods may be challenging during optimization without the help of anchors as references. Unfortunately, the anchor-based methods always suffer from the careful design of the anchors, degrading the robustness to complex scenes. To address the above-mentioned problems, we propose a novel hidden anchor mechanism (HAM) especially for scene text detection. The predictions of anchors are innovatively regarded as hidden layers, and the weighted sum of the predictions is integrated into a direct regression-based network. Hence, the architecture of our HAM still has the characteristic of simplicity as with direct regression-based methods. Moreover, it is easier to optimize anchors as references with this type of method than with direct regression-based methods. In this way, our network can take advantage of both direct regression and anchor mechanisms. In addition, we decouple three kinds of one-dimensional anchors from three-dimensional anchors, greatly reducing the number of anchors in text bounding box matching without performance degradation. We also propose a post-processing technique for long text detection, named iterative regression box (IRB), which takes a few additional computational costs and can be easily generalized to other methods. Experiments on several public datasets demonstrate that the proposed method achieves state-of-the-art performance. Code is available athttps://github.com/hjbplayer/HAM.