# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
###### Abstract  
The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network's low-level and high-level semantic information to enhance the network's effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017-Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection. s 2  
other objects with similar appearances to street sign text are often mistaken as street sign text by the detection network, which reduces the accuracy of the text detection, Eliminating the complex background beyond the target to be detected is also a concern of scholars in various countries [4]. In addition, natural scene street sign text is significantly affected by factors of the natural environment, such as lighting, obstructions, and shooting angles, making text detection in street sign images very challenging. With the advancement of deep learning text detection algorithms, the focus of text detection has shifted from horizontal scene text detection [5] to more challenging slanted text detection and arbitrary shape text detection [6]. Network frameworks such as Mask R-CNN [7] have achieved good results in scene text detection, but most Mask R-CNN-based methods use simple single-scale convolutional layers for stacking, which does not make full use of high-level semantic information for the detection of multi-scale and arbitrarily shaped text. In addition, some methods incorporate the attention mechanism in feature pyramid networks (FPNs) and replace ordinary convolution with inflated convolution [8], but they still do not make full use of the low-level information of the network. This approach ignores the importance of low-level information for smaller text and text edge detection. In summary, the current methods for detecting street sign text in natural scenes still have the following problems: (1) it is not possible to remove the influence of complex backgrounds on street sign text detection, and (2) the high-level semantic information of the feature extraction network and the contextual information in the network are not fully utilized. Therefore, this study aimed to obtain a natural scene text detection method that can effectively reduce the influence of complex backgrounds and make full use of the contextual information of the feature pyramid network to achieve the effective detection of street sign text. Through observation, it was found that although the shape of the text changed due to inclination and bending, the relationship between the pixel values of the same text region did not change.