适用于文字检测的候选框提取算法

在文字检测的相关研究中，针对文字的候选框提取方法并未得到广泛关注与深入挖掘。一方面由于文字本身结构和一般物体具有较强的差异性，另一方面由于文字对检测的精度要求高。本文提出了一种针对文字的候选框提取算法，该算法首先利用全卷积网络进行快速预测文字区域，有效地减少了候选框的搜索范围，然后针对文字特性对EdgeBox算法进行改进，使之适用于自然场景文字候选框的提取。此外，本文在两个自然场景文字检测的标准数据集上对该算法进行了评测，并与其他已有的候选框提取方法进行了比较。实验结果表明本文方法相较其他算法，具有更好的性能和鲁棒性。

Proposal Extraction Method for Text Detection

In the study of text detection, the proposal extraction method is not widely concerned and deeply studied, due to the structure of the text and otherness of the general object and the high precision requirement of text detection. In this paper, we propose a proposal extraction method for text detection. The proposed method firstly utilize the fully convolutional network to predict the text regions, which can effectively reduce the search range of the proposal extraction. Then, the EdgeBox algorithm is improved to make it suitable for the text proposal extraction in natural scenes. In addition, the proposed method is evaluated on two standard natural scene text detection benchmarks, and compared with other existing methods. Results show that the proposed method has better performance and robustness than other methods.