# Chinese Character Boxes: Single Shot Detector Network for Chinese Character Detection
## 4 Experimental Results
### Detection Performance Evaluation  
**Detection performance according to the default box size and ratios** The size of the default box specifies the range from the smallest to the largest of the data-sets. The minimum and maximum sizes were set to 10 and 650 respectively. To determine the aspect ratio of the default box, k-mean clustering which was the method used in [31] was applied and the mean IoU according to the number of clusters was compared as shown in Figure 8. Considering the characteristics of the characters written in the document, it is expected that the aspect ratio will be similar. The aspect ratio distribution of clustering using 8 clusters is [1.02, 1.0, 1.03, 1.07, 1.08, 1.6, 1.68, and 1.97] and the actual aspect ratio distribution of the data-set is narrow. On the other hand, a wide range of aspect ratio showed better performance than those with a tight aspect ratio given as a result of clustering and broader aspect ratio. This is because there is no uniformity in that the shape and size of each document vary according to the author. Most aspect ratios of data are within a narrow range but better performance is achieved with fewer default boxes because it does not consider variations in the aspect ratio depending on the author rather than using a wider range of aspect ratios. In addition, a wide range of aspect ratios show good results when actually receiving a new type of data-set as input.  
**Performance comparison based on the number of layers and whether or not the base network is pre-trained** As a base network, Resnet-34 which is most frequently used in detector network is used up to a \(\frac{1}{8}\) scale. Because pre-trained models are the trained weights for extracting more general features pre-trained sets are not used to obtain more optimized features to detect Chinese characters. Tables 2 and 3 present their performance. As a result, when using a pre-trained network, there is no direct relationship but the performance improves with increasing number of layers. On the other hand, when using a network that is not pre-trained, the performance is reduced when using more than 16 layers. Although a comparison of the two experimental results did not yield any significant results,  
Figure 6: Example of problem caused by an unrestricted random parameter in augmentation.  
Figure 7: Example of caoshu data-set augmentation using a random crop.  
training together by merging the character data-set and caoshu data-set was used. Table 5 shows the performance of the detector by training the augmentation character data-sets and caoshu data-sets using the restricted random parameter. At this time, the character data-set and caoshu data-set each had approximately 30,000 images and a total of 60,000 images were used for training. The detector performance was evaluated using optimized conditions such as data-set and network structure, NMS, aspect ratio, and size of the default boxes that solved the above-mentioned problems. To confirm the performance based on the 16 layers that showed the best performance in the previous experiment, only 10, 16, and 22 layer deviations were compared. As a result, it showed the best performance with a 96.1% detection rate and 0.89 FPPC when using 16 layers and showed better performance than the conventional SSD with 69.4% and 6.57 FPPC. The detection rate is defined by Equation (5). Figure 9 presents the detection results of conventional SSD and the detection results using the proposed CCB-SSD network. In the case of conventional SSD, the tuning part was set in the same condition as the proposed CCB-SSD network and the entire network structure such as the number of filters and layers was used as the conventional SSD architecture.  
\[\textit{detection rate}\ (\%)=\frac{\textit{detected GTs}}{\textit{number of GTs}} \tag{5}\]