## 2 Related Work  
In the field of historical document digitization, several studies on non-Chinese handwritten texts have provided valuable insights. For example, in the study of ancient Hebrew ostraca, [PERSON] et al. [9] propose a variational approach for stroke restoration, modeling each character as a trajectory of a pen with a variable radius. They optimize a cubic spline representation via gradient descent, guided by manually sampled anchor points, to structurally reconstruct over one thousand characters dating back to the 8 th-7 th century BCE. TexRGAN [10], on the other hand, introduces an end-to-end restoration framework based on Generative Adversarial Networks (GANs), which is the first to simultaneously handle struck-out and underlined handwritten text within a single deep model. By incorporating both spatial and structural loss functions and adopting a weakly supervised training scheme, the model significantly improves robustness and OCR accuracy under various types of textual distortions. To meet the digitization needs of medieval Latin dictionaries, [PERSON] et al. [11] design a complete pipeline for handwritten text recognition, covering lemma localization, extraction, and transcription. Their approach integrates image segmentation for preprocessing with a Transformer-based architecture composed of vision encoders and a GPT-2 decoder and applies extensive data augmentation techniques to enhance generalization. In the context of damaged Latin inscriptions, [PERSON] et al. [12] propose several neural network-based methods for restoring missing text, including Transformer models based on the Ithaca architecture, masked language models, diffusion-based models, and blank language models, forming a cross-disciplinary digital restoration framework. Additionally, facing the challenges of diverse scripts and complex layouts in 12 th-15 th century Latin and French manuscripts, [PERSON] et al. [13] employ CRNN-CTC architectures for handwritten text recognition and explore cross-model fusion strategies to further improve recognition performance. Although these methods provide effective solutions for Western scripts, their direct applicability to Chinese characters remains limited due to the significantly higher visual complexity, dense stroke patterns, and structural variability of Chinese writing. In addition, in strong steles and ancient books often present challenges related to non-linear degradation. For ancient Chinese books and stone inscriptions, existing research primarily focuses on single-task or dual-task settings, such as text detection, character recognition, or glyph restoration. One representative work, proposed by [PERSON] et al. [5], addresses glyph restoration from stone inscription images using pre-cropped single-character inputs. To tackle the high self-similarity between foreground characters and background textures, they introduce a character image segmentation framework that combines stacked U-Nets [14] with generative adversarial networks (GANs), aiming to enhance character extraction and restoration accuracy. The research in [4] tackles visual corruption in inscription rubbings caused by weathering and erosion. This work presents a multimodal benchmark framework for ancient text restoration, including the construction of the Chinese Inscription Rubbing Image (CIRI) dataset and a diffusion-based restoration pipeline. The proposed pipeline integrates a global restoration module for structural recovery, a local refinement module for ambiguous characters, and a text rectification module for recognition, clustering, rendering, and semantic correction. [PERSON] et al. [8] focus on character detection in Han dynasty bamboo slip images, which present challenges such as dense character layouts, scale variations, and minimal spacing. Their model, based on a U-Net [14] architecture, learns both character regions and boundary information to improve detection and separation accuracy.