## 3 Materials and Methods
### Target Detection Models  
The aim of target detection is to detect whether an image contains certain targets and to identify and locate those targets accurately; it is an application direction in the field of computer vision. The most popular target detection algorithms can be divided into two classes. The first class of algorithms is called \"two-step\" detection algorithms, in which a dense set of candidate regions is first sampled on the feature map, and then the candidate regions are classified and regressed with high detection accuracy, typically by region-based CNN (R-CNN) [16], Fast R-CNN [17], or Faster R-CNN. The other class of algorithms is called \"one-step\" detection; i.e., direct sampling and regression on a multi-layer feature map, generating the class probability and location coordinate values of the object and outputting them in a single step, which is fast. Typical algorithms include you only look once (YOLO) [18] and single shot multibox detector (SSD) [19]. R-CNN, proposed by [PERSON] et al. in 2014, is one of the first algorithms to apply deep learning techniques to target detection. The data features of the candidate regions are synthesized and classified using a support vector machine. Compared with some traditional algorithms for target detection, R-CNN replaces the feature extraction part with a deep neural network, which is a breakthrough in terms of accuracy and speed. However, the method is computationally overloaded and trades off a large amount of resources and runtime for improved accuracy. Fast R-CNN is an upgraded version of R-CNN that adds a region of interest (ROI) pooling layer after the last convolutional layer of the CNN. This allows each input image of the network to be of arbitrary size and only one feature extraction for each image, greatly \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Number of Categories** & **Number of Character Samples** \\ \hline [PERSON] and [PERSON], 2019 & Unknown & 60,000 \\ [PERSON] et al., 2020 & Unknown & 6230 \\ [PERSON], 2020 & 17 & 8500 \\ [PERSON]\_T (our) & 111 & 164,789 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of other datasets of same class. Figure 4: Image obtained via the image cropping method. improving efficiency. In addition, Fast R-CNN uses Softmax instead of support vector machines for multi-task classification, which significantly improves the target detection efficiency again. However, it has the problem of time-consuming selection of candidate regions. The main feature of Faster R-CNN, published in 2017, is the change of the candidate region extraction method, i.e., the use of the region proposal network (RPN), which allows CNNs to generate candidate regions directly, instead of the selective search method used previously. The RPN can automatically extract candidate regions quickly and efficiently, and the alternating training method of RPN and Fast R-CNN can improve the accuracy of target detection while greatly reducing the time spent on target detection. The overall process can be categorized into four steps: candidate region generation, feature extraction, classification, and position refinement. The overall structure of the Faster R-CNN is shown in Figure 5.  
The core idea of YOLO is to solve the object detection problem as a regression problem, using the whole image as the input to the network, and training and detecting in one network throughout. Published in 2016, YOLO has undergone multiple generations: YOLOv1, YOLOv2 [20], YOLOv3 [21], YOLOv4 [22], and YOLOv5. YOLOv1 directly predicts candidate frame locations, creatively combining recognition and localization into one. YOLOv2 uses offset prediction to speed up the convergence of the network. YOLOv3 provides two major improvements, namely, the use of residual models to deepen the backbone network and the use of the feature pyramid network (FPN) architecture for multi-scale detection.