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This tutorial demonstrates a practical implementation of an end-to-end document intelligence pipeline using deepDoctection, integrating layout analysis, OCR, and table extraction for downstream RAG workflows. By extending the framework with custom entity recognition services, developers can generate structured JSONL data essential for advanced retrieval-augmented generation systems. The guide provides actionable steps for configuring specific models like DocLayNet and Table Transformer to handle complex document structures efficiently.

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Building an End-to-End Document Intelligence Pipeline with deepDoctection

MarkTechPostAugust 23, 20263 min read

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. The post Building an End-to-End Document Intelligence Pipeline with deepDoctection appeared first on MarkTechPost.

In this tutorial, we implement a document intelligence pipeline with deepDoctection 1.2.x that combines layout detection, table structure recognition, OCR, reading-order reconstruction, annotation linking, and structured export in a single workflow. We configure the analyzer explicitly with DocLayNet-based layout detection, Table Transformer structure recognition, and DocTR OCR, then inspect the resulting Page objects to understand how deepDoctection represents text, figures, tables, relationships, provenance,…

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