ModelRefs / Multimodal Document Intelligence Pipeline — Tutorial
Multimodal Document Intelligence Pipeline — Tutorial
Build a pipeline that ingests PDFs, scans, and forms and emits validated structured JSON. Covers What is Document Intelligence, Pipeline Architecture.
Overview
Build a pipeline that ingests PDFs, scans, and forms and emits validated structured JSON.
Level: Advanced. Estimated reading time: 60 minutes.
What is Document Intelligence?
Document intelligence extracts structured data from unstructured or semi-structured documents: PDFs, scanned invoices, medical forms, contracts, receipts.
A production pipeline handles the messiness of real documents: variable layouts, handwritten fields, multi-column tables, low-resolution scans, and mixed languages.
Pipeline Architecture
A multimodal document pipeline runs in four stages:
1. INGEST — load PDF/image, normalize resolution, split into page images 2. UNDERSTAND — pass page images to a multimodal model; prompt it to extract target fields 3. VALIDATE — run extracted JSON through a typed schema (Pydantic); catch missing/wrong-type fields 4. STORE — write validated records to a database or downstream system; route failures to human review
Fallback Strategy
Always design a fallback chain:
1. Try multimodal model extraction (fast, no preprocessing needed) 2. If confidence < threshold → try OCR + layout parser (Tesseract, AWS Textract, Azure Form Recognizer) 3. If still failing → route to human review queue with the source document
Don't skip the human fallback — it's your safety net for edge cases and your training data source for model improvement.
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