Digital Paper: How PDF Stalled the Evolution of Documents

Abstract

Despite its limited support for semantic representation, the PDF format remains the dominant medium for document publication due to its visual fidelity, portability, and widespread acceptance. As a result, contemporary documentprocessing workflows often treat PDFs as digital paper rather than structured data, creating a persistent gap between human-oriented presentation and machine-based processing requirements. PDF was designed primarily to preserve visual appearance, not document semantics or logical structure. Consequently, information extraction from PDF files is inherently complex and error-prone, as it requires reconstructing content hierarchy, relationships, and reading order from low-level layout cues. This limitation significantly constrains automated analysis, information retrieval, and AI-driven processing, particularly in domains such as law and public administration. This paper presents a PDF processing approach developed within the CAISE project that transforms arbitrary textbased PDF documents into a structured JSON representation. The solution preserves layout-related metadata, spatial localisation of elements, and embedded resources such as images and tables, while addressing character encoding corruption commonly found in real-world PDFs. The resulting structured data provide a foundation for reconstructing logical document structure and enabling downstream semantic processing. The proposed approach illustrates how presentation-oriented documents can be systematically transformed into machine-processable representations, mitigating the long-standing limitations of PDF-basedworkflows and supporting advanced document analysis and knowledge extraction.