本文已被：浏览 1539次 下载 556次
Received:February 28, 2020
Received:February 28, 2020
中文摘要: 由于自然场景中的图像背景复杂、文字排列不规则、光照条件不确定等因素，文字检测难度较大，且传统检测方法的效果并不理想。在研究文字分割检测方法PSENet （Progressive Scale Expansion Network）的基础上，提出了一种针对自然场景文字检测的改进方法。该方法由卷积神经网络提取特征模块，再通过渐进式规模扩张对文字区域进行分割检测。改进点主要是使用高精度的语义分割网络RefineNet（Refinement Network），对卷积网络特征提取模块进行优化，且增加较多的残差连接及链式池化，提高网络对文字区域的检测精度。通过对数据集ICDAR2015的实验结果对比，表明所提出的改进算法在精度方面略高于改进前，且能更好地解决文字粘连问题。
Abstract:Due to the problematic scene background,irregular arrangement of text,and uncertain lighting conditions in natural scenes,text detection is difficult,and the traditional detection method is not ideal.In the study of the text segmentation detection method Progressive Scale Expansion Network (PSENet),an improved method for text detection in a natural scene is proposed.The improved model mainly uses the convolutional neural network to extract feature modules and performs segmentation detection on the text area through progressive scale expansion.The improvement points mainly uses a high-precision semantic segmentation network(RefineNet),optimizing the volume and network feature extraction modules,adding more residual connections and chain pooling,and improving the network's detection accuracy of the text area.Comparing the experimental results on the data set (ICDAR2015),the proposed improved algorithm is slightly more accurate than the previous algorithm and can better solve the problem of text conglutination.
keywords: text detection image segmentation feature fusion
文章编号：20211014 中图分类号：TP183;TP389.1 文献标志码：
基金项目:
Reference text: