To address the challenges in detecting scores and related information on test papers, such as complex backgrounds and diverse handwriting styles, this paper proposes an improved algorithm based on YOLO11n. A Difference-of-Gaussians downsampling module, DOG-Stem, is designed to enhance edge feature extraction. Moreover, a lightweight grouped detection head, EfficientHead, is constructed, reducing parameters and computational complexity by 10.5% and 15.9%, respectively, while maintaining high performance. Finally, the WIoU loss function is introduced to accelerate model convergence. Experimental results demonstrate that the improved model achieves an mAP50 of 96.3% and an mAP50-95 of 68.6% on the test set, representing increases of 1.3% and 1.6% over the original YOLO11n. The proposed model exhibits superior precision and robustness.