Building an End-to-End Document Intelligence Pipeline with deepDoctection
Deep dive: turning unstructured documents into RAG-ready structured data with deepDoctection.
Why it matters
A practical engineering tutorial on building document intelligence pipelines—layout analysis, OCR, table extraction, and entity recognition—for practitioners scaling RAG systems. Useful reference for teams handling document ingestion at scale.
The key facts
10 to knowdeepDoctection framework used for end-to-end pipeline
Layout analysis, DocTR OCR, table extraction covered
Custom entity recognition service implementation
JSONL output for RAG workflows
Document intelligence as applied tool (not model release)
deepDoctection for layout analysis
DocTR OCR integration
Table extraction capability
Custom entity recognition services
End-to-end pipeline architecture
Go to the source
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Publisher excerpt: Build an end-to-end document intelligence pipeline with deepDoctection. This tutorial covers configuring layout analysis, DocTR OCR, and table extraction, while demonstrating how to implement custom services for entity recognition and generate structured JSONL data for your RAG workflows.