What is a vision language model and how does it apply to document processing?

A vision language model (VLM) reads document images the way a person does: understanding layout, text, tables, and visual elements together. Unlike OCR that only extracts raw text, a VLM trained on Datature Vi understands document structure and can answer questions like 'What is the total amount on this invoice?' directly from the image.

How are VLMs better than traditional OCR for document extraction?

Traditional OCR extracts text line by line without understanding context or structure. A VLM trained on Datature Vi understands that a number next to 'Total' on an invoice is a monetary amount, not a product code. This contextual understanding eliminates the brittle template-matching that plagues rule-based document processing.

Why should teams fine-tune a VLM for documents instead of using a generic model?

Generic models struggle with industry-specific document formats, custom tables, and specialized terminology. Datature Vi fine-tunes models on your actual document types, so the system learns your invoice layouts, report templates, and field naming conventions. A fine-tuned model on Datature Vi extracts data from your documents with higher accuracy than any general-purpose tool.

What document types can Datature Vi process?

Datature Vi processes any document rendered as an image: invoices, receipts, contracts, forms, engineering drawings, handwritten notes, medical records, and insurance claims. Upload documents as JPEG, PNG, or TIFF. The VLM learns your specific document formats during fine-tuning.

How does Datature Vi extract structured data from documents?

Train phrase grounding annotations that link natural language field names to their visual locations (for example, 'invoice number' mapped to its bounding box). Structured JSON output is baked into the model during fine-tuning, so every extraction follows your exact schema with typed fields -- no post-processing regex or parsing needed.

How much training data does Datature Vi need for document extraction?

Start with 100 to 300 annotated document images per document type. For each document, annotate key fields using phrase grounding (linking field names to locations) and VQA pairs (question: 'What is the invoice date?' answer: '2024-03-15'). Datature Vi's IntelliScribe pre-labeling cuts annotation time by 3-5x.

Which base model works best for document extraction on Datature Vi?

Qwen2.5-VL 7B with its 128K context length handles multi-page documents and dense tables well. LoRA training on Datature Vi keeps memory usage low (3-5x less than full fine-tuning) while maintaining strong extraction accuracy. For simpler single-page documents, Qwen2.5-VL 3B offers faster inference.

Can Datature Vi handle handwritten documents?

Yes. Fine-tune on annotated samples of your handwritten content. VLMs process handwriting as visual patterns, not OCR character matching, so they handle varied handwriting styles, crossed-out text, and annotations in margins. Datature Vi's VQA training teaches the model to answer questions about handwritten content directly.

How does Datature Vi ensure consistent JSON output from document models?

Vi's fine-tuning process uses system prompts and RLHF to align the model's output format during training. This means structured JSON output is a model-level capability -- every extraction produces valid, typed JSON with the fields you define, regardless of whether you deploy on Vi Cloud, Vi SDK, or any other path. No post-processing needed.

Can Datature Vi process documents in multiple languages?

Yes. Qwen2.5-VL supports multilingual input natively. Fine-tune on annotated documents in each target language using Datature Vi's annotation tools, and the model learns language-specific extraction patterns. This is particularly effective for organizations processing documents across multiple regions.

How does Datature Vi integrate with existing document workflows?

Deploy on Vi Cloud for instant API access, or use the Vi SDK for on-premise processing. Every deployment path exposes the same OpenAI-compatible REST API, so you can swap a Vi fine-tuned model into existing document processing pipelines by changing one line: the base URL. Structured outputs match your downstream system's expected format out of the box.

How does Datature Vi handle sensitive document data?

For regulated industries, Datature Vi's Enterprise plan supports VPC and on-premise deployment. After training, the Vi SDK processes documents entirely on your local infrastructure with no data leaving your network. Redact personally identifiable information from training data before annotation.

Document Extraction AI with VLMs

THE PROBLEM.

Enterprises process millions of documents each year. Manual data entry costs $5-25 per document. Template-based OCR breaks on layout variations, handwritten notes, and rotated stamps.

$5-25

Cost range per document when using manual data entry operators for structured field extraction

Field extraction accuracy achieved by fine-tuned VLMs on invoices, contracts, and complex forms

0.3s

Average processing time per document including OCR, field extraction, and structured JSON output

No Domain KnowledgeCan't Read ImagesFine-Tuned on Vi

THE BASELINE

GENERAL MODELS LACK DOMAIN EXPERTISE.

GPT-4o, Claude, and Gemini have broad knowledge, but zero understanding of your specific domain, standards, or terminology.

✗No usable data extracted

Why it fails:

•Extracts some fields but with inconsistent accuracy; vendor name partially correct, amounts occasionally garbled

•Output format varies between calls; no consistent JSON schema for downstream integration

Foundation Model (GPT-4o / Claude / Gemini)

You

Prompt

Extract all fields from this invoice image: vendor, date, line items, amounts, and total

image_001.jpg

Response

This appears to be a commercial invoice document. I can see it contains information about a transaction. The vendor appears to be "Apex Industrial" or similar. There are several line items listed with amounts. The total appears to be around $4,000-4,500. However, I cannot guarantee the accuracy of these extractions, and the output format may vary between requests. For reliable automated extraction, a dedicated document processing pipeline would be more appropriate.

THE GAP

GENERAL MODELS CAN'T READ YOUR IMAGES.

Even with reference documents attached, foundation models cannot reliably interpret domain-specific visual data.

✗Partial extraction with errors

Why it fails:

•Vendor name wrong ("Acme Corp" vs actual "Apex Industrial Supply Co.")

•Reads date correctly ~80% of the time but occasionally returns wrong month or drops the day

Foundation Model + RAG (Context-Augmented)

You

Prompt + Reference Data

Extract all fields from this invoice: vendor, date, line items, amounts, total

image_001.jpg

invoice_template_schema.json30_labeled_invoice_examples.zip

Response

Based on the provided schema and examples, I can extract the following: Vendor appears to be "Acme Corp" or similar. Date is likely in the upper-right area, possibly March

THE ANSWER

YOUR DATA, FINE-TUNED ON VI.

A model trained on your private data sees exactly what you see. Your domain. Your standards. Production-ready.

✓Full extraction. Zero manual review needed.

98.6%

Field Accuracy

100%

Schema Compliance

85ms

Latency

Datature Vi — Inference

LIVE

Model: ft-invoice-extract-qwen7b-v2

INVOICE_LINE_ITEM · 95.0%

INVOICE_NUM · 95.0%

INVOICE_DATE · 95.0%

INVOICE_DUE_DATE · 95.0%

BILL_TO_ADDRESS · 95.0%

AMOUNT_DUE · 95.0%

TOTAL_BFR_TAX · 95.0%

PYMENT_TERMS · 95.0%

Vi Output

{"invoice_number": "#898146", "invoice_date": "2021-06-24", "due_date": "2021-09-22", "bill_to": {"name": "John Lambert, Johnson LLC", "address": "1216 Suarez Tunnel Apt. 333, South Victoriachester, GA 93458"}, "line_items": [{"id": 1421, "desc": "Pink blue felt craft trinket box", "qty": 2, "price": 2.46, "total": 4.92}], "subtotal": 4.92, "tax": 0.27, "shipping": 5.65, "total_due": 10.84, "payment_terms": "NET 90 days"}

98.6%

Field Accuracy

100%

Schema Compliance

85ms

Latency

Scroll to continue

↓

FROM RAW IMAGES TO
PRODUCTION MODEL.

YOUR OUTPUT, YOUR FORMAT.

Structured reports, raw JSON, concise alerts. Control the output with system prompts and refine it with RLHF. The model speaks the way your application needs it to.

Generate an extraction report for this invoice with all identified fields, non-OCR elements, and confidence scores

PROCESS DOCUMENTS AT SCALE.

Vi accepts documents from scanners, email inboxes, and cloud storage. The model extracts structured data as valid JSON matching your schema. No rigid templates. Results push to your ERP, accounting system, or data warehouse via API. Guided JSON decoding guarantees output structure. NIM containers handle thousands of documents per hour.

Vi SDK and NVIDIA NIM containers provide OpenAI-compatible APIs. Connect to any system that speaks REST.

Enterprise Ready

View Trust Center

SOC 2 Type II

Audited annually

HIPAA Compliant

PHI safeguards

AES-256 + TLS 1.2+

Encrypted at rest and in transit

G2 High Performer

4.9/5 with 47 reviews

Your Data, Your Models

Full ownership and export

INTELLIGENT DOCUMENT DATA EXTRACTION

THE PROBLEM.

GENERAL MODELS LACK DOMAIN EXPERTISE.

GENERAL MODELS CAN'T READ YOUR IMAGES.

YOUR DATA, FINE-TUNED ON VI.

FROM RAW IMAGES TO
PRODUCTION MODEL.

Ground Key Fields

Train on Your Document Types

Extract with Structured Output

YOUR OUTPUT, YOUR FORMAT.

PROCESS DOCUMENTS AT SCALE.

DOCUMENT EXTRACTION
FAQ.

SEE IT
IN ACTION.

Schedule a Demo

Start Free

RELATED USE CASES.

Screenshot to HTML

MRI Report Generation

Shelf & Planogram Audit

TRY THIS USE CASE.
START FREE.

INTELLIGENT DOCUMENT DATA EXTRACTION

THE PROBLEM.

GENERAL MODELS LACK DOMAIN EXPERTISE.

GENERAL MODELS CAN'T READ YOUR IMAGES.

YOUR DATA, FINE-TUNED ON VI.

FROM RAW IMAGES TOPRODUCTION MODEL.

Ground Key Fields

Train on Your Document Types

Extract with Structured Output

YOUR OUTPUT, YOUR FORMAT.

PROCESS DOCUMENTS AT SCALE.

DOCUMENT EXTRACTIONFAQ.

What is a vision language model and how does it apply to document processing?

How are VLMs better than traditional OCR for document extraction?

Why should teams fine-tune a VLM for documents instead of using a generic model?

SEE ITIN ACTION.

Schedule a Demo

Start Free

RELATED USE CASES.

Screenshot to HTML

MRI Report Generation

Shelf & Planogram Audit

TRY THIS USE CASE.START FREE.

FROM RAW IMAGES TO
PRODUCTION MODEL.

DOCUMENT EXTRACTION
FAQ.

SEE IT
IN ACTION.

TRY THIS USE CASE.
START FREE.