## 4 Dataset and Evaluation Protocol
### Harmonic Evaluation Metric  
To comprehensively evaluate the performance of the proposed model on the three subtasks, we design a set of task-specific metrics as well as joint evaluation indicators. For the text detection task, we adopt Hmean [23] as the primary evaluation metric to assess both the accuracy and completeness of text region localization. Hmean is defined as the harmonic mean of detection precision and recall, as given by the following formula:  
\[\text{Hmean}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{14}\]  
Precision indicates the proportion of predicted bounding boxes that are successfully matched to ground-truth boxes, while recall measures the proportion of ground-truth boxes that are correctly detected by the model. During evaluation, we determine matches by computing the Intersection over Union (IoU) between predicted and ground-truth boxes, using a fixed threshold of 0.5. A detection is considered correct only if the IoU exceeds this threshold and the match is unique. Precision reflects the model's ability to avoid false positives, whereas recall captures its capacity to cover all relevant text regions without omissions. Since these two metrics may conflict, Hmean serves as a balanced measure that considers both, providing a more comprehensive assessment of the overall performance of the text detection module. In the character recognition task, we use Character Accuracy (CA) as the evaluation metric to measure the model's performance at the character classification level. This metric is defined as the ratio of correctly recognized characters to the total number of characters. \(N_{\text{correct}}\) denotes the number of characters whose predicted labels exactly match the ground truth, and \(N_{\text{total}}\) represents the total number of characters to be recognized. The evaluation is conducted on a per-character basis, which directly reflects the classification accuracy of the character recognition module. \[\text{CA}=\frac{N_{\text{correct}}}{N_{\text{total}}} \tag{15}\]  
In the glyph restoration task, we adopt mean Intersection over Union (mIoU) and Root Mean Square Error (RMSE) as joint evaluation metrics to assess the differences between the predicted and ground-truth images from the perspectives of structural similarity and pixel-level accuracy. mIoU measures the overlap between the predicted and reference regions and reflects the accuracy of structural reconstruction. RMSE quantifies the overall pixel-wise error between the two images, where a lower value indicates a closer match to the ground truth. To provide a more comprehensive evaluation of restoration quality, we introduce a combined score termed GlyphScore. This metric averages the normalized values of mIoU and RMSE, balancing both structural fidelity and fine-grained detail preservation. A higher GlyphScore indicates better performance in both restoring the underlying glyph structure and reconstructing pixel-level details. We introduce two joint evaluation metrics to assess the synergy between tasks to comprehensively evaluate the overall performance of the model under a multi-task learning framework. Hmean-CA is designed to measure the combined performance of the model on character detection and recognition. This metric is computed as the harmonic mean of Hmean and CA. Hmean-CA considers both the localization accuracy of the detection module and the classification accuracy of the recognition module, thus reflecting the model's ability to coordinate text extraction with semantic understanding. As the harmonic mean is sensitive to lower values, this metric penalizes poor performance in either subtask, encouraging the model to maintain a balanced and stable performance across the two tasks. The definition is as follows:\[\text{Hmean-CA}=\frac{2\cdot\text{Hmean}\cdot\text{CA}}{\text{Hmean}+\text{CA}} \tag{16}\]  
HCG serves as a joint metric for all three tasks, evaluating the overall performance of character detection, recognition, and glyph restoration simultaneously.