Intelligent Document Classification In Online Library Management Using Hybrid Deep Learning Model

Abstract

Intelligent document classification has gained significant importance in online library management, as it facilitates efficient organization and retrieval of electronic documents. Traditional document classification methods involve manual labeling and sorting, which can be time-consuming and prone to errors. In recent years, deep learning models have been successfully applied to document classification tasks, achieving high accuracy and reducing the need for manual intervention. However, these models often require massive amounts of labeled data to train effectively. This paper proposes a hybrid deep-learning model for intelligent document classification in online library management. The model combines the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to effectively classify a wide range of document types, including text, images, and multimedia files. The CNN component extracts features from documents, while the RNN component learns temporal dependencies between documents. This allows more accurate classification of documents with complex structures and varying lengths. We evaluate the performance of our model using a real-world dataset of documents from an online library. The results show that our hybrid model outperforms traditional and single-model approaches, achieving a classification accuracy of over 95%. Furthermore, the model can adapt to new document types with minimal retraining, making it a versatile and robust solution for intelligent document classification in online library management.