If you’ve been circling deepDoctection’s documentation trying to figure out how the pieces actually fit together, this tutorial is the missing manual. It walks through a complete document intelligence pipeline using version 1.2.x, combining layout detection, table structure recognition, OCR, reading-order reconstruction, and annotation linking in a single workflow.
The setup is explicit and opinionated. The analyzer is configured with DocLayNet for layout detection, Table Transformer for table structure, and DocTR for OCR. Configuration overrides disable PDFMiner in favor of the deep learning stack, set word-matching rules to intersection-over-area with a 0.3 threshold, and enable layout linking between figures/tables and their captions. The tutorial also includes a compatibility patch for Transformers and PEFT, which is the kind of practical detail that saves you an afternoon of debugging.
What makes this guide genuinely useful is that it goes beyond the default pipeline. It demonstrates registering custom object types and implementing a PipelineComponent that extracts monetary and date entities while classifying documents by their tabular characteristics. That’s a real-world extension pattern, not toy code. The tutorial then assembles a custom pipeline manually with ServiceFactory, explores filtering and service rollback, serializes processed pages, and transforms annotations into ordered JSONL chunks for downstream RAG and retrieval systems.
For anyone building document processing workflows that need to feed into LLM-based retrieval, the JSONL export step alone is worth the read. The tutorial doesn’t just show you how to run the default analyzer—it shows you how to bend the framework to your own document types and extraction needs. The code is presented as a Jupyter notebook with helper functions for handling directories, PDFs, and individual images, which addresses a common gotcha: single images require passing bytes explicitly, not just a path.
