## 3 Methodology
### Fine-Grained Text Region Annotation Strategy  
In object detection and classification tasks, a target region annotation map is used to indicate which areas of an image contain the objects of interest (e.g., text). Traditional methods typically construct such maps based on bounding boxes. Specifically, for each pixel in the image, a value of 1 is assigned if the pixel lies within the annotated region, indicating that it belongs to the target area. Otherwise, a value of 0 is assigned, indicating background. The target region annotation map \(M(x,y)\) is defined as follows:  
\[M(x,y)= \begin{cases}1,&\text{if }(x,y)\in\mathcal{R}_{\text{bbox}}\\ 0,&\text{otherwise}\end{cases} \]  
where \((x,y)\) denotes the pixel position and \(\mathcal{R}_{\text{bbox}}\) represents the annotated region provided by bounding boxes. Although this strategy is commonly used in general object detection and recognition tasks, it presents certain limitations in text-related scenarios. First, text region annotations are typically based on rectangular bounding boxes, which not only cover the actual character strokes but also inevitably include surrounding background areas, such as inter-character spacing, noise around the text, image textures, and even printing or carving artifacts. These non-text pixels, although labeled as part of the target region, do not carry meaningful semantic information and may interfere with the model's ability to accurately learn the structure of the text during training. Second, in page-level images, text usually occupies only a small portion of the image. This is particularly true in historical documents or stone inscription images, where text regions coexist with large and complex backgrounds, and the characters are dense and structurally detailed. In such cases, directly using the entire annotated region as the training supervision signal may reduce the model's focus on critical stroke features and lead to the learning of background patterns, thereby affecting the recognition accuracy. Furthermore, characters exhibit highly structured spatial patterns, such as the relative positions among horizontal, vertical, and diagonal strokes. However, traditional coarse-grained region annotations are insufficient for capturing such fine-grained details and only provide a broad, weak supervisory signal. This overly relaxed supervision may cause the model to learn redundant or irrelevant information, making it difficult to focus on the true boundaries and structural features of characters, which in turn limits its generalization ability and robustness in complex scenarios. Figure 3 illustrates this issue. The green rectangle represents the bounding box used to localize a textual region. A common approach to converting this box into a supervision map is to fill the entire area with a uniform foreground label, resulting in what we refer to as a box-based mask annotation (indicated by the gray-green shaded area). This method provides weak supervision, as it assumes all pixels within the bounding box contribute equally to the semantic content of the region. In reality, only a subset of these pixels corresponds to actual character strokes. To address the mentioned limitations of traditional target region annotation methods in text-based scenarios, we propose a fine-grained instance mask-based annotation strategy for the classification task. The goal is to improve the model's learning efficiency and recognition accuracy in the presence of complex backgrounds. This method introduces structure-aware supervision at the glyph level by leveraging character-level segmentation labels, also referred to as glyph masks. In the supervision signal, only the pixels that correspond to character strokes are preserved. Specifically, we construct a binary mask where pixels belonging to the strokes are assigned a value of 1, and all other pixels, such as those in inter-character spaces and background regions, are assigned a value of 0. This produces a more discriminative and semantically precise target region annotation map.