To address these challenges, this work emphasizes glyph restoration alongside text recognition. A benchmark dataset of handwritten Chinese text is constructed, covering a wide range of noisy scenarios with synthetic and real-world samples. It supports line-level and page-level granularity and includes a glyph annotation tool with high-quality annotations for related research. Methodologically, a baseline model named ResLU is proposed for line-level text, jointly optimizing recognition and glyph restoration via multi-task learning with the combined loss. This improves recognition robustness under noise while enabling glyph reconstruction. To overcome the limitations of line-level methods in handling layout and granularity, a character-level, page-wise framework named UniText is further introduced. It integrates detection, recognition, and glyph restoration, remains efficient across diverse layouts and noise, and aligns well with the character-as-class nature of Chinese script.

Extensive experiments on synthetic and real-world handwritten text datasets show that the proposed models achieve comparable performance to existing state-of-the-art methods in text detection and recognition, while demonstrating superior effectiveness in glyph restoration, indicating good practicality and scalability.