DEEP LEARNING APPROACHES FOR EXTRACTING STRUCTURED INFORMATION FROM UNSTRUCTURED BUSINESS DOCUMENTS

Zhao Morales Rao

DEEP LEARNING APPROACHES FOR EXTRACTING STRUCTURED INFORMATION FROM UNSTRUCTURED BUSINESS DOCUMENTS

Authors

Zhao Morales Rao

Senior Research Scientist, Germany.

Keywords:

Deep Learning, Information Extraction, Unstructured Documents, Document Understanding, Structured Data

Synopsis

Deep learning has revolutionized the task of transforming unstructured business documents—such as invoices, receipts, and contracts—into structured data usable for automated analytics and business intelligence. This paper surveys core deep learning techniques (e.g., CNNs, RNNs, transformers) for key information extraction and structure recognition from diverse document formats. We discuss model architectures, training challenges, dataset considerations, and performance indicators. Two conceptual diagrams and two synthesis tables summarize the task pipeline and representative models. The paper concludes with future directions including multimodal and large scale pretrained models.

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