Document Information Extraction

Document information extraction studies how to convert documents into structured fields, entities, relations, events, and tables that can support search, analysis, and decision-making. Common approaches combine OCR and layout analysis with supervised sequence taggers, schema-guided language models, multimodal models, retrieval, and deterministic validation. Work spans receipts and forms as well as long clinical, financial, legal, and scientific documents, with benchmarks increasingly testing noisy inputs, layout understanding, source grounding, and output completeness. A central challenge is balancing flexible semantic interpretation with exact values, boundaries, and reliable evidence, while keeping systems efficient and robust across document types.