DAC-Net: Dual-Attention Collaborative Network for Robust Shui Script Detection

Abstract

Shui Script is an ancient pictographic writing system with significant cultural and historical value. Automatic character detection is a fundamental step toward the digital preservation of Shui manuscripts, yet remains challenging due to slender strokes, densely distributed small characters, and complex background interference. To address these challenges, we propose DAC-Net, a lightweight dual-attention collaborative network based on YOLOv9. Specifically, the Dual-Attention Feature Enhancement Module (DAFEM) enhances weak stroke representations and preserves fine structural details, while the Decoupled Spatial-Channel Attention Fusion Module (DSAF) decouples spatial localization and channel semantic modeling to suppress background interference and improve feature discrimination. Extensive experiments demonstrate that DAC-Net achieves competitive performance against representative detection methods under multiple IoU thresholds while maintaining real-time inference efficiency. Notably, the performance improvement of DAC-Net over YOLOv9 is merely about 1% at IoU = 0.6 and IoU = 0.7. Robustness evaluations under brightness variation, motion blur, Gaussian noise, and partial occlusion further verify the effectiveness and generalization capability of the proposed method.