## 5 Experiments
### Ablation Study
This improvement is primarily attributed to the complementary relationship among tasks; the glyph restoration task provides pixel-level structural supervision, enhancing the model's ability to perceive local information such as character contours and stroke details. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Exp** & **Models** & **mIoU \(\uparrow\)** & **RMSE \(\downarrow\)** & **Prec. \(\uparrow\)** & **Rec. \(\uparrow\)** & **CA \(\uparrow\)** & **GS \(\uparrow\)** & **Hm. \(\uparrow\)** & **Hm.-CA \(\uparrow\)** & **HCG \(\uparrow\)** \\ \hline
1 & UniText & **0.6691** & 0.1426 & **0.9822** & **0.9588** & **0.8805** & 0.7632 & **0.9704** & **0.9232** & **0.8629** \\
2 & bbox-sel & 0.6195 & 0.1538 & 0.9746 & 0.9050 & 0.8037 & 0.7328 & 0.9385 & 0.8659 & 0.8165 \\
3 & all-pixel & 0.6683 & **0.1418** & 0.9655 & 0.9236 & 0.5405 & **0.7633** & 0.9441 & 0.6875 & 0.7110 \\
4 & w/o \(\mathcal{L}_{\text{res}}\) & - & - & 0.8462 & 0.9077 & 0.6102 & - & 0.8759 & 0.7193 & - \\
5 & w/o \(\mathcal{T}_{\text{res}}\) & - & - & 0.9588 & 0.9479 & 0.8764 & - & 0.9533 & 0.9132 & - \\
6 & w/o \(\mathcal{T}_{\text{rec}}\) & 0.6237 & 0.1498 & 0.9810 & 0.9502 & - & 0.7370 & 0.9654 & - & - \\
7 & w/o \(\mathcal{T}_{\text{det}}\) & 0.6657 & 0.1451 & - & - & 0.6075 & 0.7603 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiments on stroke-aware loss and joint tasks on the test set. Best results are highlighted in bold. “Prec.” and “Rec.” denote precision and recall, respectively. “GS”, “Hm.”, and “Hm.-CA” represent GlyphScore, Hmean, and Hmean-CA. Figure 7: Comparison of classification loss across three pixel-level supervision strategies. As a result, it offers richer and more accurate structural features to support both detection and recognition tasks. It is noteworthy that, compared to completely removing the glyph restoration head (Exp. 5), removing only its loss function while retaining the branch structure (Exp. 4) leads to a more pronounced drop in detection and recognition performance. This phenomenon reveals the issue of gradient interference in multi-task architectures. When an auxiliary task lacks explicit supervision during training, its branch may still generate uncontrolled or even disruptive gradients during backpropagation, which can negatively affect the optimization of the shared backbone and undermine the learning of the primary tasks. These results not only validate the positive role of the glyph restoration task in three-task joint learning, but also underscore the necessity of caution when designing unsupervised auxiliary branches in multi-task models, in order to avoid potential negative effects on the shared backbone network. In the extended task ablation experiments, we remove the recognition task (Exp. 6) and observe a decline in segmentation performance. This indicates that the recognition task provides auxiliary semantic supervision, which helps improve segmentation accuracy. However, the detection metrics remain relatively stable, suggesting that the recognition task has a limited impact on detection. This is primarily because the recognition and detection tasks share a large portion of low-level and mid-level features, and are ultimately processed by separate task-specific heads. Under this architecture, the detection task does not directly depend on the recognition task. When the detection task is removed (Exp. 7), the recognition performance drops significantly, with the CA score decreasing from 0.8805 to 0.6075. Although the recognition and detection tasks appear structurally independent, with shared features and separate heads, the detection task plays a critical guiding role during training. It provides region-level supervision that encourages the shared feature maps to focus on relevant text regions. This implicit guidance is essential for the recognition task to accurately extract and classify textual content. Without detection supervision, the recognition task becomes more vulnerable to background noise, leading to a substantial performance drop.