## 5 Experimental Results
### Analysis of the Proposed Scheme
#### 5.3.1 Key Character Detection  
Key character detection is an important step in the proposed scheme. Here, \(N^{KC}\) is 16, and we evaluate the performance of the proposed character detection algorithm. Figure 10 illustrates the results of the proposed key character detection and keyword-detection algorithms. The left and right columns of Figure 10 are the ROIs and the results, respectively. Figure 11 illustrates the local detection results of the proposed scheme. In Figures 10 and 11, each rectangle with a solid line is the bounding box of RetinaNet for key character detection, and each rectangle with a dashed line is the output of the proposed keyword detection. As we can see in Figure 10, these ROIs have different image contents, some ROIs may have noise, and the quality of some key characters, e.g., \"\(\mathbb{H}^{\prime}\)\" (Surname) and \"\(\mathbb{H}^{\prime}\)\" (First Name), may vary in the ROIs. Though the image quality of some ROIs may not be good, the proposed scheme can detect and recognize the key characters in document images with different contents. From Figure 10b4,b5, the key characters, \"\(\mathbb{H}^{\prime}\)\" (Apply), \"\(\mathbb{H}^{\prime}\)\" (Apply), and \"\(\mathbb{J}\)\" (Applicant), can be detected well even when their fonts are different in ROIs. This means that ResNet in RetinaNet can effectively extract the useful and representative features from the training data, and these features are robust even when noise or poor visual quality happens. Furthermore, though the key characters \"\(\mathbb{H}^{\prime}\)\" (Build) have different sizes in Figure 10b1,b2 most can be correctly detected by using the proposed scheme. This means that ResNet can extract useful feature maps, and FPN is useful to yield the multi-scale feature maps for detecting key characters with different sizes. Therefore, the results show that the proposed scheme based on RetinaNet can detect these key characters well, even in situations of poor visual quality, different fonts, and different sizes. Figure 12 shows two cases with incorrect detection results. The left and right columns of Figure 12 are the ROIs and the key character detection results, respectively. Dashed-line circles are used to point out wrong detection results. As we can observe in Figure 12b1, the visual quality of some key characters \"\(\mathbb{H}^{\prime}\)\" (Apply) is poor, so they cannot be detected correctly. On the top of Figure 12b2, one key character, \"\(\mathbb{H}^{\prime}\)\" (Building), is not detected. In addition, some key characters are incorrectly classified in Figure 12. For example, two characters \"\(\mathbb{H}^{\prime}\)\" (prefix indicating ordinal number) and \"\(\mathbb{H}^{\prime}\)\" (Floor) are misclassified as the key characters \"\(\mathbb{H}^{\prime}\)\" (Building) and \"\(\mathbb{H}^{\prime}\)\" (Use) in Figure 12b1, respectively. Similarly, one character \"\(\mathbb{H}^{\prime}\)\" (Median) and one digit \"0\" are wrongly determined as key characters \"\(\mathbb{H}^{\prime}\)\" (Apply) and \"\(\mathbb{H}^{\prime}\)\" (Use) in Figure 12b2, respectively. Figure 9: Results of ROI initialization; (**a1**–**a3**): input, (**b1**–**b3**): output. Figure 10: Results of key character detection and keyword detection. (**a1**–**a5**): ROI; (**b1**–**b5**): results. Figure 11: Local detection results of Figure 10b1-b5 for the proposed scheme: (**Lb1–Lb5**): local results of Figure 10b1–b5. Figure 12: Two cases with wrongly detected characters in the key character detection. (**a1–a2**): ROIs; (**b1–b2**): results. Here, mAP is utilized to assess the performance of the proposed scheme for all key characters. To compute the mAP rate, the thresholds of confidence and IoU are determined as 0.3 and 0.5, respectively. Figure 13 shows the AP rates of the proposed key-character-detection algorithm. As we can observe in Figure 13, the AP rates of the common key characters, \"\(\mathbb{H}\)\" (Surname), \"\(\mathbb{Z}\)\" (First Name), \"\(\mathbb{H}\)\" (Live), and \"\(\mathbb{H}\)\" (Site), are at least 0.9.