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.


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    Title :

    Detecting Text in Scene and Traffic Guide Panels With Attention Anchor Mechanism


    Contributors:
    Hou, Jie-Bo (author) / Zhu, Xiaobin (author) / Liu, Chang (author) / Yang, Chun (author) / Wu, Long-Huang (author) / Wang, Hongfa (author) / Yin, Xu-Cheng (author)


    Publication date :

    2021-11-01


    Size :

    5628414 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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