Leveraging computer vision and natural language processing for efficient metadata extraction in digitized books

Abstract

High-quality book metadata improves digital libraries by supporting cataloging, searchability and automated classification. However, metadata generation from scanned books remains challenging due to limited annotated data sets and optical character recognition (OCR) limitations. This study aims to propose a deep learning-based framework for automatic metadata extraction from digitized books by integrating computer vision and natural language processing (NLP). The framework uses MobileNetV2 to classify title pages, table of contents (ToC) pages and content pages. EfficientDet detects metadata-related regions, such as titles and author information. OCR extracts text from these regions, followed by named entity recognition (NER) and regular expressions to refine the extracted metadata. The framework was evaluated using a custom data set of 188 books published between 1800 and 2021, comprising 857 annotated pages. The page classification model achieved 97.16% accuracy. For object detection, the model obtained average precision (AP) and average recall (AR) scores of 71.7% and 42.0% for title pages, 55.1% and 33.4% for ToC pages and 87.9% and 54.4% for content pages, respectively. This study contributes a scalable framework that combines page classification, object detection, OCR, NER and rule-based refinement to reduce manual metadata extraction in digital library management. Future research can expand multilingual data sets, improve robustness to OCR noise and explore end-to-end learning for richer bibliographic metadata extraction.