Knowledge Extraction and Structured Processing of Technical Standard PDF Documents

Technical standards, serving as repositories of industry knowledge and critical enablers of core competitiveness, have garnered significant attention in the context of industrial digitization. However, conventional general-purpose conversion for PDF technical standards documents often fall short due to the diverse content and complex logical structures inherent in these documents. To tackle this issue, we propose a novel solution for knowledge extraction and automatic structured processing of technical standard PDF documents. our approach focuses on key tasks such as metadata extraction, structural function recognition of information units, paragraph heading structure recognition, and chart extraction. We develop discriminant models tailored to different objects and scenarios, design an XML-based structure, and achieve structured output. The experiment results with dataset from the “Smart Distribution Network” field in the electric power industry of China shows that the proposed algorithm has taken the lead in metadata extraction and title recognition and has good practicality.