# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 2 Related Work
[22] proposed an origin-independent coordinate regression loss and text instance accuracy loss on a pixel-based text detector, which alleviated the impact of the target vertex ordering and predicted the location of text instances more accurately. [PERSON] et al. [23] proposed a semi-supervised scene text detection framework (Semi Text) using a pre-trained supervised model and an unlabeled dataset to train a scene text detector that was both robust and accurate. Although deep learning methods that can acquire high-level features through convolutional operations have achieved good results in recent years, these methods rely excessively on the adjustment of network parameters and lack flexibility. Some researchers still rely on traditional methods, such as MSER methods, for preliminary text region extraction from images. [PERSON] et al. [24] developed a contrast-enhanced maximally stable extremum region algorithm (CE-MSER) and combined it with CNNs to increase the robustness of the detection network. [PERSON] et al. [25] used the characteristics of DCT, important information in the image is found by selecting multiple channels, and texture distribution is studied based on statistical measurement to extract features. Then a deep learning model is proposed to eliminate false positives and improve the performance of text detection. [PERSON] et al. [26] used a combination of MSER and cloud of line distribution (COLD) approaches to extract candidate regions of image text, and the extracted features were then sent to the CNN for extraction. This method exhibited better detection effects under low light. Although the above methods have made some progress in the field of text detection, they still have the following problems in the detection of severely slanted and different-sized texts in complex scenes. (1) Constrained by the candidate frame, regression-based networks are less effective at detecting text with large tilt angles. (2) Segmentation-based methods do not work well for detecting small text instances with low contrast and text instances with complex layouts in images. (3) Most of the methods are not able to effectively remove the interference of complex backgrounds and strong light on text detection, resulting in false detection. For street sign text detection in natural scenes, compared with existing methods, our proposed method was designed to eliminate the impact of complex backgrounds and illumination on detection, as well as the effects of different sizes and shapes of text regions in the images during detection. The detection process is shown in Figure 1. Although the text in natural scenes is greatly affected by the lighting and shooting angles, the pixel values and pixel relationships between text remain constant during character changes, so a multi-channel MSER method is proposed to preprocess the image. Compared with the traditional MSER method, the method in this paper uses multiple channels are used to extract the maximally stable extremal regions, reducing the impact of complex scenes and strong light on text area detection. In addition, an improved DBNet network is proposed to further detect the image of the text area preliminarily extracted by the multi-channel MSER method. Compared with the previous works, the newly added FPRE module and the HLFE module can make full use of the information of the feature extraction network, and improve the detection effect of the network on different shapes, sizes, and oblique texts.