Research on a Method for Recognizing Text on Book Spines in Libraries Based on Improved YOLOv11 and Optimized PaddleOCR

The growing scale of libraries necessitates intelligent management solutions, particularly for book inventory tasks. To address the challenge of book spine recognition in dense, text-heavy environments, this study proposes an integrated approach combining an enhanced YOLOv11 model with a hyperparameter-optimized PaddleOCR framework. The methodology involves augmenting the YOLOv11 object detector with a Channel-Spatial Dual Attention Mechanism (CBAM) to better extract spine texture features and suppress interference from adjacent books. For the text recognition stage, PaddleOCR’s hyperparameters were task-optimized by adopting the RecAug data augmentation strategy, adjusting the curved text detection loss weight, expanding the character dictionary, and modifying the input image size. Experimental results on a self-constructed Book Spine Dataset show that the improved YOLOv11 achieved a segmentation accuracy of 97.4%, a 2.1% increase over the baseline, while reducing computational load and parameters. The optimized PaddleOCR saw its character error rate drop from 8.6% to 3.2%. Consequently, the end-to-end system attained a 96.8% single-book recognition accuracy in real bookshelf scenarios, demonstrating that this targeted strategy significantly enhances performance for intelligent library management.