Detecting incidental scene text is a challenging task because of
multi-orientation, perspective distortion, and variation of text size, color
and scale. Retrospective research has only focused on using rectangular
bounding box or horizontal sliding window to localize text, which may result in
redundant background noise, unnecessary overlap or even information loss. To
address these issues, we propose a new Convolutional Neural Networks (CNNs)
based method, named Deep Matching Prior Network (DMPNet), to detect text with
tighter quadrangle. First, we use quadrilateral sliding windows in several
specific intermediate convolutional layers to roughly recall the text with
higher overlapping area and then a shared Monte-Carlo method is proposed for
fast and accurate computing of the polygonal areas. After that, we designed a
sequential protocol for relative regression which can exactly predict text with
compact quadrangle. Moreover, a auxiliary smooth Ln loss is also proposed for
further regressing the position of text, which has better overall performance
than L2 loss and smooth L1 loss in terms of robustness and stability. The
effectiveness of our approach is evaluated on a public word-level,
multi-oriented scene text database, ICDAR 2015 Robust Reading Competition
Challenge 4 "Incidental scene text localization". The performance of our method
is evaluated by using F-measure and found to be 70.64%, outperforming the
existing state-of-the-art method with F-measure 63.76%.

本文提出了一种基于卷积神经网络的新方法 Deep Matching Prior Network，通过使用四边形滑动窗口和快速计算多边形区域的共享蒙特卡罗方法来检测具有更紧致四边形的场景文本。本方法的性能在公共场景文本数据集上进行了评估，表现出比现有的 state-of-the-art 方法更好的 F-measure 值。