In order to improve the robustness of text detector on scene text of various scales, a single shot text detector that combines local and non-local features is proposed in this paper. A dilated inception module for local feature extraction and a text self-attention module for non-local feature extraction are presented, and these two kinds of modules are integrated into single shot detector (SSD) of generic object detection so as to perform multi-oriented text detection in natural scene. The proposed modules make a contribution to richer and wider receptive field and enhance feature representation. Furthermore, the performance of our text detector is improved. In addition, compared with previous text detectors based on SSD which classify positive and negative samples depending on default boxes, we exploit pixels as reference for more accurate matching with ground truth which avoids complex anchor design. Furthermore, to evaluate the effectiveness of the proposed method, we carry out several comparative experiments on public standard benchmarks and analyze the experimental results in detail. The experimental results illustrate that the proposed text detector can compete with the state-of-the-art methods.

为了提高文本检测器在各种比例场景文本上的鲁棒性，提出了一种结合局部和非局部特征的单镜头文本检测器。提出了一种用于局部特征提取的扩张起始模块和用于非局部特征提取的文本自我关注模块，并将这两种模块集成到通用目标检测的单次检测器中以执行多方向自然场景中的文本检测。所提出的模块为更广泛的接受领域做出了贡献，并增强了特征表示。此外，我们的文本检测器的性能得到了改善。此外，与以前的基于SSD的文本检测器相比，该检测器可以根据默认框对正样本和负样本进行分类，我们利用像素作为参考，与地面真相进行更精确的匹配，从而避免了复杂的锚设计。此外，为了评估该方法的有效性，我们在公共标准基准上进行了几次比较实验，并详细分析了实验结果。实验结果表明，提出的文本检测器可以与最新技术竞争。