Research on a Method for Recognizing Text on Book Spines in Libraries Based on Improved YOLOv11 and Optimized PaddleOCR
Year: 2025
Type: article
Abstract: 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.
Source: Electronics
Authors: Zheng Li, Bingzhen Guo, Dengcong Mu
Institution: Huaibei Normal University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416783602