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

Area of Science:

• Computer Science
• Information Science

Background:

• Automating information extraction from Portable Document Format (PDF) documents offers significant benefits across various fields like healthcare and law.
• Current PDF information extraction solutions struggle with accuracy, domain adaptability, and implementation complexity.

Purpose of the Study:

• To systematically review existing approaches and trends in PDF information extraction and storage.
• To propose a novel conceptual framework to overcome the limitations of current methods.

Main Methods:

• A systematic literature review was performed adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
• Analysis of identified literature to categorize dominant methodologies in PDF information extraction.

Main Results:

• Three primary methodological categories were identified: rule-based systems, statistical learning models, and neural network-based approaches.
• Key limitations include the inflexibility of rule-based systems, data scarcity for learning-based methods, and issues like hallucinations in large language models.

Conclusions:

• A nine-component conceptual framework is proposed to enhance the accuracy, adaptability, and usability of PDF information extraction systems.
• This framework integrates various modules for comprehensive PDF data processing and knowledge retrieval.