场景文本检测是指在场景图像中定位文本区域并用文本框将其标记出来。随着移动互联网的快速发展和智能手机等移动终端设备的日益普及,场景文本检测技术的研究得到了高度重视和广泛的应用。近年来,随着以卷积神经网络为代表的深度学习的兴起,场景文本检测的研究有了新的发展。然而,由于以下两个因素,场景文本检测仍然是一项非常具有挑战性的任务。首先,自然场景中的图像往往具有复杂的背景,这很容易干扰检测过程。其次,自然场景中的文本非常多样化,水平的,偏斜,直,和弯曲的文本,所有这些都可能出现在同一场景中。当卷积神经网络提取特征时,感知场有限的卷积层不能很好地对全局语义信息进行建模。因此,本文进一步提出了一种基于双分支特征提取的场景文本检测算法。本文通过残差校正分支(RCB)扩大了感受野,以获取具有更大接受域的上下文信息。同时,为了提高使用功能的效率,提出了一种基于FPN的两分支注意特征融合(TB-AFF)模块,为了结合全球和当地的注意力来精确定位文本区域,增强网络对文本区域的敏感性,并准确检测自然场景中的文本位置。在本文中,进行了几组对比实验,并与目前主流的文本检测方法进行了对比,所有这些都取得了更好的结果,从而验证了改进方法的有效性。
Scene text detection refers to locating text regions in a scene image and marking them out with text boxes. With the rapid development of the mobile Internet and the increasing popularity of mobile terminal devices such as smartphones, the research on scene text detection technology has been highly valued and widely applied. In recent years, with the rise of deep learning represented by convolutional neural networks, research on scene text detection has made new developments. However, scene text detection is still a very challenging task due to the following two factors. Firstly, images in natural scenes often have complex backgrounds, which can easily interfere with the detection process. Secondly, the text in natural scenes is very diverse, with horizontal, skewed, straight, and curved text, all of which may be present in the same scene. As convolutional neural networks extract features, the convolutional layer with limited perceptual field cannot model the global semantic information well. Therefore, this paper further proposes a scene text detection algorithm based on dual-branch feature extraction. This paper enlarges the receptive field by means of a residual correction branch (RCB), to obtain contextual information with a larger receptive field. At the same time, in order to improve the efficiency of using the features, a two-branch attentional feature fusion (TB-AFF) module is proposed based on FPN, to combine global and local attention to pinpoint text regions, enhance the sensitivity of the network to text regions, and accurately detect the text location in natural scenes. In this paper, several sets of comparative experiments were conducted and compared with the current mainstream text detection methods, all of which achieved better results, thus verifying the effectiveness of the improved proposed method.