occlusions and shadows [2; 3]. These factors collectively impair the clarity and continuity of textual content, posing significant obstacles to traditional optical character recognition methods, which typically assume clean, well-aligned, and high-contrast inputs [4]. In recent years, with the rapid development of deep learning and computer vision technologies, modern neural networks have achieved remarkable progress in image processing tasks and are increasingly applied to the automatic recognition and analysis of ancient books and stone inscriptions. Some existing studies [1; 4; 5; 6; 7; 8] have explored the use of detection, recognition, or reconstruction techniques in the processing of historical documents. However, these methods typically focus on specific tasks and are often trained on cropped and well-aligned line-level text or character images. As a result, they struggle to handle the combined challenges found in full-page images, which often include various types of noise and interference. A few approaches attempt to process entire pages directly, yet they often assume that ancient texts exhibit regular structures and standardized layouts. These methods usually rely on rectangular bounding boxes for character detection, which limits their ability to handle more complex real-world scenarios such as changes in camera angle, character rotation, or skew, and flexible text arrangement. To address the limitations of existing methods, such as task specificity, reliance on cropped line or character inputs, and restricted detection box shapes, we propose a unified multi-task learning framework for character detection, recognition, and glyph restoration in page-level images of historical books and stone inscriptions. This framework enables the joint modeling and processing of entire pages under complex natural conditions, and is particularly suited for challenging scenarios involving varying camera angles, uneven lighting, and irregular text arrangements. The proposed architecture is built upon a shared backbone network, with an interactive multi-scale decoding module and task-specific output branches. The glyph restoration branch outputs a segmentation map of character structures to recover the visual appearance of individual glyphs. Meanwhile, the detection and recognition branches jointly perform  
Figure 1: Sample images of Chinese ancient books and stone inscriptions collected from publicly available photos via Google Image Search. The Chinese characters shown in the images are original historical texts, which serve as the target content for handwritten text understanding. geometric localization and category prediction of characters through convolutional and fully connected layers. By introducing a mechanism for feature sharing across tasks and designing specialized decoders for each task, the framework effectively integrates both visual and semantic information, enabling the efficient joint modeling of character structure and meaning. In particular, to improve the learning efficiency of the recognition branch, we propose a training strategy that combines fine-grained instance-level mask supervision with target-aware classification loss. During training, the model produces pixel-level category predictions across the entire image, but classification loss is computed only within valid labeled regions. This reduces the influence of background noise on recognition and enhances the model's robustness in complex environments. Overall, the main contributions of our work are as follows:  
* We propose an end-to-end multi-task learning framework for character detection, recognition, and glyph reconstruction in page-level images of Chinese historical documents and inscriptions. * We introduce a fine-grained instance mask-based supervised signal for text regions, and propose multi-task loss guided by text mask, char category, and context position. This strategy is designed to suppress misleading gradients from non-text areas and improve the model's robustness to background noise.