## 4 Experimental Evaluation
### Model Comparison Experiments
#### 4.3.2 Results and Analysis  
In the first set of experiments, we fed the same dataset into three models--Faster R-CNN, YOLO, and SSD--for training and testing. The experimental results are listed in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental Group**} & \multicolumn{3}{c}{**Dataset Size**} \\ \cline{2-4}  & **Number of Categories** & **Number of Images** & **Number of Character Samples** \\ \hline Experiment 1 & 114 & 60,124 & 81,371 \\ Experiment 2 & 111 & 10,583 & 164,789 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Datasets information for the two sets of experiments. YOLOv5 achieved the highest mAP of 79.25%. The mAP of Faster R-CNN was slightly lower than that of YOLOv5, and that of SSD was much lower than that of the first two models. In the image testing part, it was found that Faster R-CNN could recognize the characters in the images better, whereas YOLOv5 could not fully recognize the Shui manuscript characters of the small targets and its misrecognition was more serious. SSD could not recognize the small target characters in most of the test images owing to its low accuracy. As shown in Figure 6, Faster R-CNN was able to accurately detect the complete characters in the same image and recognize them correctly. Although YOLOv5 also detected three characters, it only correctly detected two of the complete characters, missed one complete character, and incorrectly detected one incomplete character. SSD only detected one character in the image, confirming that SSD is less effective for detecting small and multi-target Shui manuscript text images. Because of the poor detection performance of small and multitarget images by SSD, we only compared YOLO and Faster R-CNN in the second set of experiments. We utilized the Shuishu_T dataset with target detection models: YOLOv5s, YOLOv5x, and Faster R-CNN. YOLOv5s and YOLOv5x are the latest YOLO series models, with the model size of YOLOv5s being much smaller than that of YOLOv5x. It is well known that the role of the backbone network is to extract the overall features of the input image, which is the key to the target detection method. We used two types of CNNs, VGG-16 [23] and ResNet-101 [24] as the backbone of the Faster R-CNN because they obtained satisfactory accuracies, both above 99%, in the classification experiments of Shui characters. This allowed us to compare the performance of different backbone networks in the \"two-step\" detection algorithm. VGG-16 is an efficient and straightforward deep CNN focused on building convolutional layers. It contains 16 convolutional layers and fully connected layers, which is relatively regular. Usually, each set of convolutional layers is followed by a pooling layer that can compress the image size. ResNet-101 is a residual network with 101 layers and a residual block. The residual block is often called a jump layer connection--that is, the output of some layers skips one or more layers directly so that the information is passed to the deeper layer of the neural network and solves the problem of network degradation caused by network deepening. Intersection over union (IOU) is the ratio of the intersection and union between the \"predicted area\" and the \"real area,\" and it is also a measure of object detection accuracy. For the tests, we calculated and averaged mAP values with IOU values equal to 0.5, 0.6, 0.7, 0.8, and 0.9. The results are shown in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mAP (\%)** \\ \hline Faster R-CNN & 76.7 \\ YOLOv5 & 79.2 \\ SSD & 25.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the first set of experiments. Figure 6: Image test results of Faster R-CNN, YOLOv5, and SSD. The results show that Faster R-CNN achieved a higher mAP than that of YOLOv5 on the test set. The Faster R-CNN model with ResNet101 as the backbone network always achieved a better mAP than the model with VGG16 as the backbone network, which achieved a maximum mAP of 87.8%.