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, and reading order. We also extend the framework by registering custom object types and implementing our own PipelineComponent for extracting monetary and date entities while classifying documents by their tabular characteristics. Finally, we assemble a custom pipeline manually with ServiceFactory, explore filtering and service rollback, serialize processed pages, and transform document annotations into ordered JSONL chunks suitable for downstream RAG and retrieval systems.

!pip install -q "deepdoctection" "transformers>=5.2.0" "timm" "python-doctr" "pdfplumber" "networkx" "lxml"

import os

os.environ["DD_USE_TORCH"] = "True"

os.environ["DPI"] = "200"