## 4 Dataset and Evaluation Protocol  
### Dataset Constructed with Poisson Blending and Relief Simulation  
Existing datasets [1, 4, 7, 21, 22, 23] related to traditional Chinese characters often lack comprehensive annotations at the character level for full-page texts, including detection, recognition, and glyph-level labeling. In addition, they rarely address practical challenges such as noise interference, multi-angle imaging, and complex backgrounds, which limit their effectiveness for training high-quality models. To address these issues, we consider the complexity of ancient books and stone inscriptions in real-world scenarios and synthesize page-level images of ancient texts with diverse fonts and backgrounds that simulate aged paper textures and stone surfaces. This approach reduces manual annotation costs and facilitates the efficient creation of a large and diverse set of character-level annotations. Each image includes quadrilateral bounding boxes for character detection, category labels, and glyph segmentation masks, supporting multi-task model training and evaluation. Specifically, we construct a synthetic dataset consisting of 14,000 images and 744 commonly used ancient character categories. Detailed statistics of the dataset are shown in Table 2.  
Figure 4 presents several examples of text images from ancient books and stone inscriptions in our dataset. The dataset includes 14 commonly used font styles, such as cursive, clerical, and running scripts. The text appears with varying degrees of slant and is combined with diverse backgrounds to simulate the visual characteristics found in real-world manuscripts and inscriptions. The background images are collected from a wide range of sources. For ancient books, the backgrounds are derived from high-resolution scans of historical documents, featuring yellowed paper textures, natural creases, and stain marks. In contrast, the stone inscription backgrounds are taken from photographs of actual steels, cliff carvings, and epitaphs, which exhibit irregular stone textures, weathering cracks, and lighting variations. To enhance the realism of the synthetic images and achieve natural integration between text and background, we apply a series of image processing techniques. To improve the visual quality of synthetic text-background images, we adopt a gradient domain-based method, known as Poisson Image Blending [24], as an alternative to the commonly used alpha blending approach. This method formulates image fusion as an optimization problem in the gradient field, allowing foreground content to be integrated seamlessly into the background in terms of lighting, texture, and tone. Alpha blending combines the foreground image \(S(x,y)\) and the background image \(B(x,y)\) by linear interpolation. The resulting image \(I(x,y)\) is computed as follows:  
\[I(x,y)=\alpha\cdot S(x,y)+(1-\alpha)\cdot B(x,y),\quad\alpha\in[0,1] \tag{7}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Chars** & **Classes** & **Images** & **Noise** & **Angles** & **Det** & **Rec** & **Gly** \\ \hline Train & 430,552 & 744 & 12,000 & Yes & Yes & ✓ & ✓ & ✓ \\ Test & 71,757 & 744 & 2000 & Yes & Yes & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the constructed dataset based on ancient manuscripts and stone inscriptions. The dataset is designed for three tasks: text detection (Det), recognition (Rec), and glyph restoration (Gly). Figure 4: Sample text images of ancient books and stone inscriptions from the constructed dataset. The Chinese characters are retained as they represent the primary visual content processed by our models. Although simple and efficient, this method often leads to visible seams when the foreground and background differ in texture, brightness, or material. As shown in Figure 5, the left image produced by alpha blending exhibits semi-transparent text and a washed-out background. The result lacks realism, and the text appears visually detached from the page.