Research on Aided Reading System of Digital Library Based on Text Image Features and Edge Computing

• Published in: IEEE access Vol. 8; pp. 205980 - 205988
• Main Authors: Shi, Yuqing, Zhu, Yuelong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: assisted reading; Cameras; Cloud computing; Computational modeling; Digital imaging; Digital libraries; Digital library; Digital systems; Edge computing; Feature extraction; Information retrieval; Knowledge acquisition; Libraries; Neural networks; Quality of service; RBF neural network; Reading; text image features
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3037349

With the rapid development of library informatization level, traditional text information retrieval service methods have been difficult to meet the needs of users. Therefore, personalized assisted reading technology attracted wider attention, which can discover hidden associations by mining user information, book information, and user operation logs, and recommend the acquired knowledge to users. In order to improve the service quality and experiences of users, this paper presents a digital library assisted reading system that combines text image features and edge computing. Firstly, the digital library reading assistance system framework is proposed based on edge computing technology, which will improve the service quality and information transmission level of the digital library. And the auxiliary reading model of the digital library is presented based on text image features and RBF neural network. Detailed simulation results verify the applicability and efficiency of the proposed scheme.