## 5 Experiments
### Text Detection and Recognition Performance Analysis  
To comprehensively evaluate the performance of the proposed UniText model on historical and stone inscription text detection tasks, we conduct comparative experiments against several mainstream text detection methods on a test set of 2000 images. The results are presented in Table 4. In terms of the key detection metric Hmean, UniText achieves a score of 0.9704, which is notably higher than DB [26] (0.7955) and EAST [27] (0.9603), and is comparable to the more accurate FCENet [28] (0.9772) and PSENet [29] (0.9847), demonstrating competitive detection capabilities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Hmean**\(\uparrow\) & **Precision**\(\uparrow\) & **Recall**\(\uparrow\) & **FPS**\(\uparrow\) \\ \hline Ours & 0.9704 & 0.9822 & 0.9588 & **74.6142** \\ DB [26] & 0.7955 & 0.9068 & 0.7085 & 27.1825 \\ EAST [27] & 0.9603 & 0.9892 & 0.9331 & 44.8303 \\ PSENet [29] & **0.9847** & **0.9997** & **0.9702** & 35.0545 \\ FCENet [28] & 0.9772 & 0.9932 & 0.9616 & 39.5791 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection performance comparison between the proposed UniText model and mainstream detection methods on the test set.  
It is worth noting that, unlike other methods that focus solely on text detection, UniText additionally performs character recognition and glyph restoration, enabling a more integrated form of end-to-end text understanding. Although it incorporates multiple tasks, UniText achieves an inference speed of 74.6 FPS under a batch size of 1, which is faster than FCENet [28] (39.6 FPS) and PSENet [29] (35.1 FPS), with relative improvements of approximately 1.9\(\times\) and 2.1\(\times\), respectively. These results suggest that UniText achieves a reasonable trade-off between detection accuracy, inference efficiency, and multi-task capability, making it a potentially suitable choice for practical applications.  
Figure 8 presents the visualization results of our model on the historical document image (Figure 8a), the stone inscription image (Figure 8b), and a real-world scene image (Figure 8c). It can be observed that the model handles multi-oriented character-level detection and recognition in complex backgrounds effectively. In Figure 8a, although the handwritten style of the historical document exhibits distinct historical characteristics and the page contains visual noise due to aging, the model is able to accurately localize character boundaries and produce clear recognition results. In Figure 8b, we present a stone inscription image that poses additional difficulties such as low foreground-background contrast, erosion of character edges, and uneven illumination. UniText successfully detects and recognizes each character instance with high spatial precision. Furthermore, Figure 8c illustrates the model's performance on a real-world image captured under natural scene conditions. This image contains text with varying font sizes and is taken from a non-frontal angle. Despite these challenges, the model accurately detects individual characters and maintains robust recognition performance. This demonstrates the model's good generalization ability and robustness to real-world distortions.