A review on knowledge and information extraction from PDF documents and storage approaches

Automating the extraction of information from Portable Document Format (PDF) documents represents a major advancement in information extraction, with applications in various domains such as healthcare, law, or biochemistry. However, existing solutions face challenges related to accuracy, domain adaptability, and implementation complexity.

A systematic review of the literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine approaches and trends in PDF information extraction and storage approaches.

The review revealed three dominant methodological categories: rule-based systems, statistical learning models, and neural network-based approaches. Key limitations include the rigidity of rule-based methods, the lack of annotated domain-specific datasets for learning-based approaches, and issues such as hallucinations in large language models.

To overcome these limitations, a conceptual framework is proposed comprising nine core components: project manager, document manager, document pre-processor, ontology manager, information extractor, annotation engine, question-answering tool, knowledge visualizer, and data exporter. This framework aims to improve the accuracy, adaptability, and usability of PDF information extraction systems.