## 2 Related Work
To correct this issue, they proposed a WordSup model that can be trained with weak supervision to perform character-level detection on text-line and word-level annotated datasets. [PERSON] et al. [23] [PERSON] et al. [24] introduced bidirectional feature pyramids to enhance the model's learning ability and achieved better detection results, but bidirectional feature pyramids significantly increase the computational load of model training and are difficult to use in practice. [PERSON] et al. [24] introduced graph CNNs to improve the prediction of link relationships between text primitives and the detection of arbitrarily shaped text regions. However, the introduction of graph CNNs brought with it greater parameters and computational effort. [PERSON] et al. [25] proposed a novel text detection CNN architecture sensitive to text scale to improve the prediction of small texts. However, multiple RPN modules complicated the module structure and introduced more parameters, and the module needed to be pre-designed with anchors. Table 1 summarizes the strengths and weaknesses of previously studied text region detection models. Analysis of classical models and literature [23; 24] suggests that current text region detection models are steadily improving, and the shapes of text regions that can be detected are becoming increasingly broad. However, these enhancements come at the cost of increasingly complex network structures, larger models, a larger number of parameters, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Year** & **Model** & **Advantages** & **Disadvantages** \\ \hline
1 & 2016 & CTPN [14] & \
\begin{tabular}{c} Good detection for long text \\ areas \\ \end{tabular} &
\begin{tabular}{c} Cannot detect text areas with large tilt angles; a large number of parameters and operations \\ \end{tabular} \\ \hline
2 & 2017 & SegLink [15] & Can detect inclined text areas &
\begin{tabular}{c} Easily misidentifies dense text as a single \\ whole area \\ \end{tabular} \\ \hline
3 & 2018 & TextField [21] & \
\begin{tabular}{c} Can detect text areas with \\ curvature \\ \end{tabular} &
\begin{tabular}{c} The post-processing process is complicated \\ \end{tabular} \\ \hline
4 & 2021 & [PERSON] et al. [23] & \
\begin{tabular}{c} Good detection of quadrilateral \\ text areas \\ \end{tabular} &
\begin{tabular}{c} Bidirectional feature pyramid leads to a large \\ number of model parameters and computations \\ \end{tabular} \\ \hline
5 & 2021 & [PERSON] et al. [24] & \
\begin{tabular}{c} Good detection of arbitrarily \\ shaped text areas \\ \end{tabular} &
\begin{tabular}{c} The introduction of graph convolutional neural \\ networks leads to a high parameter number and computation \\ \end{tabular} \\ \hline
6 & 2021 & [PERSON] et al. [25] & Good detect small texts &
\begin{tabular}{c} Multiple RPN modules complicate the module \\ structure and introduce more parameters; need \\ pre-designed anchors \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of select text region detection models. and more computational effort, making them difficult to apply in practice. Therefore, the goal of this study was to propose a text region detection model with the simplest possible structure, smaller parameter numbers, less computation, and better performance for the detection and recognition of text in street sign scenes. At present, research on text detection in natural scenes has made progress, but research on text detection in street signs is still lacking. In addition to the problems that exist with general natural scene text recognition, the following problems exist for street sign scene text detection. 1. Street signs contain multiple types of text, such as Chinese characters, English, numbers, and punctuation marks. 2. Differences in image brightness can be significant owing to backlit shooting or nighttime shooting. 3. Because of different shooting angles, the tilt angle on the street sign text area may be large, leading to a perspective phenomenon.