Intelligent Library Service Transformation Strategies Based on Convolutional Networks in the Information Age in the Context of Digital Transformation

Abstract

At present, the sorting work of library books heavily relies on manual labour, and the sorting process is time-consuming and laborious. To address the above issues, in the context of digital transformation in the information age, a strategy research of intelligent library service transformation based on convolutional network is proposed to achieve intelligent daily maintenance regarding libraries. In particular, the arrangement of books based on category and order is resolved. First, the Canny operator detection algorithm combined with straight line is used to extract the book spine from the book image. Second, the Faster-RCNN-based target detection algorithm is selected to locate the claim number. The positioning accuracy also reaches 96% in the positioning test, which is also better than other algorithms. The specific detection time is 100s. Then the method is used to segment the characters, and the accuracy is 97%. Finally, in the character recognition of library book request numbers, the feature extraction effect of character images based on convolutional networks is the best. Compared with 95% and 96% of other algorithms, the average recognition rate is 97%, which also improves network performance. The comparative experimental findings suggest that the convolutional network algorithm can effectively achieve the information detection and book order recognition of library common claim numbers, providing a feasible solution for library intelligence.