Are there any multimodal models that handle structured output from scanned documents accurately?

Yes — but with an important caveat: the best results usually come from a hybrid approach, not a single “all-in-one” multimodal model.

Short answer

If you want accurate structured output from scanned documents like invoices, forms, receipts, tables, or IDs, the strongest options today are typically:

1. Document AI / OCR + layout models

   • Google Document AI
   • AWS Textract
   • Azure Document Intelligence
   • Abbyy Vantage / FlexiCapture
   • Nanonets / Rossum / Mindee

2. Multimodal foundation models used carefully

   • GPT-4o / GPT-4.1 with vision
   • Claude 3.5 Sonnet with vision
   • Gemini 1.5 Pro/Flash
   • Qwen2-VL / InternVL / LLaVA variants

3. Specialized document models

   • LayoutLMv3
   • DocTR
   • Donut
   • Pix2Struct
   • TrOCR

What works best in practice

For reliable structured extraction, the most accurate pipeline is often:

• OCR / document parser to extract text and layout
• LLM or document model to map that into JSON / schema
• validation rules to check fields, totals, dates, and types

This is much more dependable than asking a general multimodal model to “read the page and output JSON” directly.

If you mean “can a multimodal LLM do it alone?”

Yes, models like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 can often extract structured data from scanned docs quite well, especially when:

• the scan is high quality
• the layout is not too complex
• the schema is fixed
• you provide a strict JSON schema / function calling
• you post-validate the output

But they still make mistakes on:

• small or blurry text
• rotated pages
• multi-column layouts
• handwritten fields
• tables with merged cells
• low-quality scans / shadows / stamps

Best choice by use case

For invoices/receipts/forms in production

Use:

• Google Document AI
• Azure Document Intelligence
• AWS Textract
• or a vendor like Rossum / Nanonets / Mindee

These are optimized for structured extraction and generally outperform general-purpose multimodal models on document-specific tasks.

For flexible extraction with custom schemas

Use:

• GPT-4o / Claude 3.5 / Gemini with:
• strict schema
• examples
• validation/retry loop

This is especially useful if the document type varies a lot.

For research/open-source

Try:

• Donut
• LayoutLMv3
• DocFormer
• Qwen2-VL
• InternVL

Practical recommendation

If accuracy matters:

• Don’t rely on raw vision-only parsing
• Prefer OCR + layout-aware extraction + schema validation
• Use a multimodal model as the reasoning/normalization layer, not the only parser

If you want, I can also give you:

• a ranked list of the best models/services for scanned document extraction
• a recommended pipeline architecture
• or a comparison table by accuracy, latency, and cost.