Precision-Based Image Classification of Library Books with Machine Learning Algorithms

DOI

10.2991/978-2-38476-533-1_2How to use a DOI?

Keywords

Image classification; Machine Learning; CNN; Keras; TensorFlow and automated classification

Abstract

This paper explores using machine learning to classify book covers for better library management, specifically using convolutional neural networks. The purpose of this system is to automate the arrangement and retrieval of books by using visual features, thus addressing the limitations of traditional metadata-based cataloging methods and reducing manual work for library staff while enhancing resource accessibility. The methodology uses a CNN model that is trained on 25 book cover images of 5 categories, namely, botany, chemistry, mathematics, physics, and zoology. The model was developed using Python with Keras and TensorFlow with layers of convolutional, linking, and unified. The model was subjected to different data augmentation techniques to make it robust and to avoid overfitting. The trained model was evaluated through image classification tests. Through the application of CNNs, this work developed an automatic system that uses advanced image classification methods to speed up cataloging operations. Additionally, the system enhances search quality by analyzing book covers. A user-friendly web application accompanies the solution, simplifying the image upload process to accommodate libraries of various sizes. The novelty of this mechanism is established in its practical application of machine learning for library automation and its contribution to artificial intelligence and library science. By using CNNs for visual content classification, the paper offers a solution for managing the expanding digital library collections. The study also points out that an issue is the variability in book cover designs, which suggests a need for better research in the future, including text detection and metadata.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sailendra Malik
AU  - Sukumar Mandal
PY  - 2025
DA  - 2025/12/31
TI  - Precision-Based Image Classification of Library Books with Machine Learning Algorithms
BT  - Proceedings of the International Conference on Smart Systems and Social Management (ICSSSM-2 2025)
PB  - Atlantis Press
SP  - 5
EP  - 17
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-533-1_2
DO  - 10.2991/978-2-38476-533-1_2
ID  - Malik2025
ER  -