Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Jianping Hu, Yongkang Yan, Zhengguang Xie

Abstract


Background: The development of technological devices has changed many facets of our lives, particularly the way we engage with information and learning. The advent of automated technology for identification has had a had a revolutionary effect on how we read and organize books within the context of books and data searches. It starts by solving the difficulties in analyzing photos of book pages by using methods such as distortion rectification and book separation. Objective: The research compares the effectiveness of the suggested method with traditional straight-line identification techniques using real-world testing. Methodology: The Skip-Gram model in Word2Vec is used to accurately represent spoken language, allowing word vectors to be generated and input data to be preprocessed for CNN. The results show that the methodology created regarding the present investigation works better than alternatives concerning accuracy and efficiency during line identification. Result:This work advances the field of book suggestion systems by presenting a strong and effective method that leverages CNNs. The findings demonstrate deep learning techniques may be used to optimize system recommendations and improve customer service and happiness in a variety of contexts. This technique creates a bridge between natural language processing and picture evaluation and opens up new possibilities for suggestion advancement along with user satisfaction.

 

Doi: 10.28991/HIJ-2024-05-01-015

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Keywords


Automatic Book Recognition; Convolutional Neural Network; Image Correction; Image Retrieval.

References


Shi, X., Tang, K., & Lu, H. (2021). Smart library book sorting application with intelligence computer vision technology. Library Hi Tech, 39(1), 220–232. doi:10.1108/LHT-10-2019-0211.

Trinquet, C. (2023). Pepper as an assistant in the library: Identifying books using machine learning. Master’s Thesis in Applied Computer Science, June 2023, Norwegian University of Science and Technology, Trondheim, Norway.

Martinez-Martin, E., Ferrer, E., Vasilev, I., & Del Pobil, A. P. (2021). The UJI aerial librarian robot: A quadcopter for visual library inventory and book localisation. Sensors (Switzerland), 21(4), 1–16. doi:10.3390/s21041079.

Locher, R. (2022). NPOCR–Needle Printer Character Recognition: Deep learning-based image ID recognition. Master Degree Project (60 ECTS) in Informatics, University of Skövde, Skövde, Sweden.

Anumula, V. R. B., Anumula, K. R. V. S. S., Anumula, M., & Damerla, S. (2024). Artificial Intelligence Using Library Information Science Professionals. International Journal of Progressive Research in Engineering Management and Science, 4(1), 591-594.

Bairagi, M., & Lihitkar, S. Empowering Libraries: AI-Driven Tools and Techniques for Digital Transformation and Sustainable Innovation. In One Day National Conference on ‘Preserving Indian Knowledge System and Envisioning the role of Libraries in NEP 2020 framework’, 10(1), 388–396.

Mupaikwa, E. (2025). The Application of Artificial Intelligence and Machine Learning in Academic Libraries. In Encyclopedia of Information Science and Technology, IGI Global, Sixth Edition, 1-18. doi:10.4018/978-1-6684-7366-5.ch041.

González‐Alcaide, G., Castelló‐Cogollos, L., Navarro‐Molina, C., Aleixandre‐Benavent, R., & Valderrama‐Zurián, J. C. (2008). Library and information science research areas: Analysis of journal articles in LISA. Journal of the American Society for Information Science and Technology, 59(1), 150-154. doi:10.1002/asi.20720.

Takher, S. (2024). Revolutionizing Agriculture Libraries in India: A Comprehensive Study on Implementing Near Field Communication (NFC) Technology for Enhanced Access and Knowledge Sharing. Asian Journal of Agricultural Extension, Economics & Sociology, 42(1), 128–138. doi:10.9734/ajaees/2024/v42i12355.

Sibiya, M., & Sumbwanyambe, M. (2019). A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering, 1(1), 119–131. doi:10.3390/agriengineering1010000.

Lin, J. D., Wu, X. Y., Chai, Y., & Yin, H. P. (2020). Structure Optimization of Convolutional Neural Networks: A Survey. Zidonghua Xuebao/Acta Automatica Sinica, 46(1), 24–37. doi:10.16383/j.aas.c180275.

Zhou, D. X. (2020). Universality of deep convolutional neural networks. Applied and Computational Harmonic Analysis, 48(2), 787–794. doi:10.1016/j.acha.2019.06.004.

Chouiekh, A., & El Haj, E. H. I. (2020). Deep convolutional neural networks for customer churn prediction analysis. International Journal of Cognitive Informatics and Natural Intelligence, 14(1), 1–16. doi:10.4018/IJCINI.2020010101.

Abidi, S. F. H., Sumukhi, T., Kumar, V., & Santhosh, B. (2021). Lung Cancer Detection Using Deep Convolutional Neural Network. International Journal of Organizational and Collective Intelligence (IJOCI), 11(4), 13-20. doi:10.4018/IJOCI.2021100102.

Yamada, A., Niikura, R., Otani, K., Aoki, T., & Koike, K. (2021). Automatic detection of colorectal neoplasia in wireless colon capsule endoscopic images using a deep convolutional neural network. Endoscopy, 53(8), 832–836. doi:10.1055/a-1266-1066.

Sunaga, Y., Natsuaki, R., & Hirose, A. (2019). Land Form Classification and Similar Land-Shape Discovery by Using Complex-Valued Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 57(10), 7907–7917. doi:10.1109/TGRS.2019.2917214.

Yurong, G., & Ke, Z. (2019). End-to-end dual-channel feature recalibration for DenseNet image classification. Chinese Journal of Image Graphics, 11(08), 126–127.

Jingdong, L., & Xinyi, W. (2019). Overview of Convolutional Neural Network Structural Optimization. Acta Automata, 14(09), 79-83.

Surono, S., Rivaldi, M., Dewi, D. A., & Irsalinda, N. (2023). New Approach to Image Segmentation: U-Net Convolutional Network for Multiresolution CT Image Lung Segmentation. Emerging Science Journal, 7(2), 498-506. doi:10.28991/ESJ-2023-07-02-014.

Surono, S., Afitian, M. Y. F., Setyawan, A., Arofah, D. K. E., & Thobirin, A. (2023). Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer. HighTech and Innovation Journal, 4(3), 531-542. doi:10.28991/HIJ-2023-04-03-05.

Roy, A. M., Bose, R., & Bhaduri, J. (2022). A fast accurate fine-grain object detection model based on YOLOv4 deep neural network. Neural Computing and Applications, 34(5), 3895–3921. doi:10.1007/s00521-021-06651-x.

Ji, S. J., Ling, Q. H., & Han, F. (2023). An improved algorithm for small object detection based on YOLO v4 and multi-scale contextual information. Computers and Electrical Engineering, 105, 108490. doi:10.1016/j.compeleceng.2022.108490.

Pang, H., Zhang, Y., Cai, W., Li, B., & Song, R. (2022). A real-time object detection model for orchard pests based on improved YOLOv4 algorithm. Scientific Reports, 12(1), 13557. doi:10.1038/s41598-022-17826-4.


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DOI: 10.28991/HIJ-2024-05-01-015

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