## 6 Experimental Evaluation
### Results  
Table 1 presents the results for _synthetic_ character detection on the 47 test pages using the different YOLO models trained 25 epochs on 30,000 synthetic pages. The character detection accuracy per epoch is depicted in Figure 3(a). The results show a surprisingly high _synthetic character detection accuracy of about_ 90% across all models despite the fact that during training of the object detection method, the models have neither seen handwritten characters nor real page backgrounds. However, Figure 3(a) also shows an overfitting to the synthetic data after 5 epochs. Besides the IoU, we also indicate the results IoU\({}_{0}\) without shrinking the detected bounding boxes. The tight bounding boxes are clearly closer to the ground truth than the raw detection results. Furthermore, the results in Table 2 show a tendency that training from scratch leads to slightly less accurate bounding boxes, i.e., a lower IoU. Among the different evaluation measures, the IoU is the most difficult for achieving a perfect score, as the detected bounding box has to fit the ground truth bounding box with pixel-accuracy, something that even different human annotators are not expected to achieve. On the other hand, precision and recall indicate very high scores despite the fact that a visual inspection of the results, for example in Figure 3(c), clearly shows deletion and insertion errors. We argue that the character detection accuracy is better suited for evaluating the detection results in a balanced way, including deletions, insertions, and the quality of the bounding box. Boxes with an IoU larger than 0.5 are typically capturing enough detail of a character, such that a subsequent character detection can be successful. Therefore, the correlation between the character detection accuracy and the character recognition accuracy is expected to be high. Regarding transcription alignment, the YOLOv5s model was able to align 872 pages (97.0%), the YOLOv5m model 875 pages (97.3%), the YOLOv5l model 861 pages (95.8%), and the YOLOv5x model 858 pages (95.4%), i.e., for almost all pages a median column and a median row could be found, allowing building a complete grid for aligning all characters of the transcription. This set of aligned pages is then used for self-training the object detection system. Table 3 and Figure 3(b) present the evaluation results for _adapted_ character detection after self-training 25 epochs on the aligned pages. The results show a significant improvement when compared with synthetic character detection, ranging from +5.27% for YOLOv5x to +7.27% for YOLOv5m, as shown in Table 4. The results in Table 3 also reaffirm the tendency that training from scratch leads to less accurate bounding boxes in terms of IoU when compared with COCO pre-trained models. Interestingly, changing the training data from synthetic data to real manuscript data leads to a loss in recognition accuracy during the first few epochs, as observed in Figure 3(b). The effect is weaker when using handwritten characters for page synthetization. However, after a few epochs, self-training leads to significantly better results when compared with the synthetic system and a plateau is reached, showing no overfitting effect. Contrary to our expectations, reducing the dictionary to the 4855 characters actually present in the 899 alignment pages and training YOLO conjointly for regression and classification did not lead to better results. All synthetization settings eventually lead to an _adapted character detection accuracy of about_ 95%. Figure 4: Evaluation of the character detection performance on the test set. The overall best result is achieved with the COCO pre-trained YOLOv5m model, resulting in the _best character detection accuracy of 96.43% and IoU of 85.13%. We compare the model with the results achieved by [PERSON] et al.