Open AccessResearch on the Extraction Method of Book Number Region Based on Bayesian Optimization and Deep Learning
Author(s) -
Qianqian Zhang,
Sun Jiang-lei,
Jing Zhao,
Zilin Xia,
Kai Zhang
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.125
Subject(s) - artificial intelligence , computer science , artificial neural network , process (computing) , identification (biology) , deep learning , bayesian optimization , machine learning , bayesian network , bayesian probability , pattern recognition (psychology) , data mining , algorithm , botany , biology , operating system
The continuous development of artificial intelligence technology has promoted the construction of smart libraries and their intelligent services. In the process of intelligent access to books, the extraction of the requested book number region has become an important part of the process. The requested book number is generally affixed to the bottom of the spine of the book, which is small in size, and the height of the book is not always the same, so it’s difficult to identify. By the way, due to the images’ resolution, shooting angle and other practical problems, the difficulty of the extraction work will be increased. To improve the identification accuracy, in this paper, Bayesian Optimization (BO) and one kind of deep neural networks ‘Faster R-CNN’ are combined for the extraction work mentioned above. The data preparation, network training, optimization variable selection, establishment of BO objective function, optimization training, and network parameter evaluation have been introduced in detail. The performance of the designed algorithm has been tested with actual images of book spines taken in the academy library and compared with several other conventional recognition algorithms. The experimental results show that the requested book number region extraction method based on Bayesian optimization and deep neural network is effective and reliable, and its recognition rate can reach 91.82%, which has advantages in both recognition rate and extraction time compared with other algorithms.