# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 3 Proposed Method
### Multi-Channel Maximally Stable Extremum Region (MSER) Model  
The pixels of the natural scene street signs have a certain relationship, which motivated us to explore the extraction of the maximally stable external regions in the natural scene street signs image. However, due to the influence of illumination in natural scenes, the brightness of different areas of the image is quite different, and only a single channel extracts a large and extremely stable area of natural scene images, which will cause some missed detections and false detections. The MSER method that is only under a single channel is difficult to adapt to the detection of street sign text affected by illumination and  
Figure 1: Overall flow chart of the method proposed in this paper. complex background. Therefore, in addition to selecting the most commonly used RGB color channel of the image, we also studied the influence of the HIS color channel of the image on the extraction of the maximally stable external regions. There are many color spaces for images, and the RGB color space is the most widely used color space. In addition, the HIS color space is often used in image processing. Therefore, in addition to selecting the most commonly used RGB color channel of the image, the experiment also studied the HIS. RGB and HIS color channel images are extracted, respectively, as shown in Figure 2. It can be found that the image in the S channel can effectively reduce the influence of strong light, and the text area is more obvious in the S color channel. The image under the H channel is greatly affected by the strong light, and the text area has been blended with the background under the influence of the strong light. The I-channel image can be replaced by different ratios of the R, G, and B channel information, as shown in (1). so the rendering effect of the image under the I channel is also similar to the effect of the RGB channel. Therefore, in the case where the maximally stable extremum region of the RGB three-channel has been extracted, it is of little significance to study the I-channel image. To explore the characteristics of the MSER method in different color channels, the Canny [27] operator was used to perform edge enhancement processing on the images of the RGB and HIS channels of the image, respectively, so that the text edges in the image become more obvious, thereby increasing the accuracy of the MSER algorithm, and then extracting the maximally stable extremum region from the image after edge enhancement, as shown in Figure 3. It can be found that the S channel can effectively extract the maximally stable extremum region under strong light, while the H channel is affected by strong light, and cannot effectively extract the maximally stable extremum region of text under strong light, and also introduces more non-text areas, which is not conducive to the further extraction of text areas using the deep learning framework. Therefore, it can be concluded that the S channel is very helpful for the initial extraction of the text area extracted by the MSER method, and the H and I channels are of little significance for the MSER method to extract the text area. Therefore, in the process of using the MSER method to extract the text area in multiple channels, we use \(R\), \(G\), \(B\), and S color channels that contain important color information. \[I=\frac{R+G+B}{3} \tag{1}\]  
where the parameter \(I\) is one of the channels of the HSI color space; \(R\), \(G\), and \(B\) are the three channels of the RGB color space. According to the following equation [9] to determine whether they belong to the MSER:  
\[\varphi(i)=\frac{|R_{i+\Delta}-R_{i-\Delta}|}{|R_{i}|} \tag{2}\]  
where \(R_{i}\) denotes a certain connected region when the threshold is \(i\), \(\varphi(i)\) denotes the area change rate of the region and \(\Delta\) is the step size of the slight change of the grayscale threshold.