Amazon Textract vs Google Document AI vs PDF Parser: Real Accuracy Test
We ran the same 100 documents through Amazon Textract, Google Document AI, and PDF Parser. Three different tools, three different approaches to document extraction. The results were eye-opening.
Here's what we found: enterprise tools from AWS and Google deliver impressive accuracy — when you can get them running. The setup alone took hours. Meanwhile, the extraction quality gap between these platforms and simpler alternatives has narrowed significantly.
Our verdict? Amazon Textract shines for AWS-native workflows. Google Document AI leads on complex multi-language documents. PDF Parser wins on time-to-value and total cost of ownership for small to medium teams.
Want to test it yourself? Try PDF Parser free — upload your first document in seconds.
What We Actually Tested
Marketing claims are easy to make. We wanted real numbers.
The test documents:
Volume: 100 documents total, processed through each platform under identical conditions.
Quality levels: We deliberately included some rough documents — faded scans, tilted images, low contrast. Real business documents aren't always pristine.
What we measured:
Amazon Textract: The AWS Native Option
Amazon Textract has been AWS's document extraction answer since 2019. It goes beyond basic OCR by identifying forms, tables, and key-value pairs automatically.
How it works: You upload documents to S3, call the Textract API, and receive structured JSON with detected text, forms, and tables. For synchronous operations, you get results in seconds. Async processing handles larger batches through job polling.
The Forms API automatically identifies labeled fields — "Invoice Number: 12345" becomes a clean key-value pair without templates. Tables API detects rows and columns, outputting structured cell data.
Pricing Reality
| Feature | Price per 1,000 pages |
|---|---|
| Detect Text (OCR only) | $1.50 |
| Analyze Document (forms + tables) | $15.00 |
| Queries (targeted extraction) | +$2.00 per query |
That $15 per thousand sounds reasonable until you add S3 storage, Lambda invocations for async processing, and data transfer costs. Our test batch cost roughly $22 for 1,000 pages including infrastructure.
First 1,000 pages free for three months on new accounts.
Our Accuracy Results
| Document Type | Textract Accuracy |
|---|---|
| Clean printed invoices | 97.2% |
| Complex multi-column tables | 91.8% |
| Scanned forms (200+ DPI) | 90.4% |
| Low-quality scans (<150 DPI) | 76.3% |
| Documents with handwritten notes | 71.2% |
Textract performed best on clean, high-contrast documents with standard layouts. The Forms API correctly identified key fields like invoice numbers and dates 94% of the time on quality documents.
Table extraction showed solid column detection but struggled with merged cells and nested headers. Several financial reports had misaligned data that required manual correction.
The Good
The Not-So-Good
Best For
Organizations already running on AWS with DevOps support, high-volume document processing needs, and workflows that benefit from native AWS integration.
Google Document AI: The ML Powerhouse
Google Document AI brings Google's machine learning expertise to document extraction. The approach centers on specialized "processors" — pre-built or custom-trained models for specific document types.
How it works: You create a processor (Invoice Parser, Form Parser, or Custom), send documents via API, and receive structured output. Google's underlying ML models handle OCR, layout analysis, and field extraction.
The processor concept is powerful but adds a configuration layer. You're not just calling an API — you're selecting which trained model to apply.
Pricing Reality
| Processor Type | Price per 1,000 pages |
|---|---|
| Document OCR | $1.50 |
| Form Parser | $30.00 |
| Invoice Parser | $30.00 |
| Custom Processor | $30.00 + training costs |
Google's pricing is higher than Textract for specialized extraction. Custom processor training adds upfront costs that vary by dataset size.
First 1,000 pages free monthly (ongoing, not trial-limited) — the most generous free tier.
Our Accuracy Results
| Document Type | Document AI Accuracy |
|---|---|
| Clean printed invoices | 98.1% |
| Complex multi-column tables | 93.4% |
| Scanned forms (200+ DPI) | 91.7% |
| Low-quality scans (<150 DPI) | 81.2% |
| Documents with handwritten notes | 74.8% |
Document AI delivered the highest accuracy in our test, particularly on invoices and multi-language documents. The Invoice Parser correctly identified vendor names, amounts, and line items with impressive consistency.
Table handling was strong. Column detection outperformed both competitors, and the structure preservation held up better on complex layouts.
Low-quality scans still caused problems, but Document AI's preprocessing seemed more resilient to image degradation.
The Good
The Not-So-Good
Best For
Organizations on Google Cloud, teams needing custom document processors, multi-language document workflows, and ML-focused groups comfortable with GCP tooling.
Seeing these accuracy numbers? The differences are smaller than you'd expect from the marketing. Test PDF Parser against your actual documents — it takes 30 seconds.
PDF Parser: The Simpler Alternative
PDF Parser takes a different approach: focused functionality without cloud platform overhead. No AWS account, no GCP project, no processor configuration. Upload a document, get structured data.
How it works: Upload a PDF (native or scanned), and the AI handles OCR, layout analysis, and field extraction automatically. Export to Excel, CSV, or JSON. API access for automation.
The design philosophy prioritizes time-to-value. Most users get their first extraction within a minute of landing on the site.
Pricing Reality
Credit-based model. No tiers, no infrastructure overhead, no surprise fees. What you see is what you pay.
The cost per document lands between $0.01 and $0.02 depending on volume. For 1,000 documents, expect $10-20 total.
No hidden S3 fees. No compute charges. No data transfer costs.
Our Accuracy Results
| Document Type | PDF Parser Accuracy |
|---|---|
| Clean printed invoices | 96.4% |
| Complex multi-column tables | 92.1% |
| Scanned forms (200+ DPI) | 89.8% |
| Low-quality scans (<150 DPI) | 77.9% |
| Documents with handwritten notes | 69.4% |
PDF Parser's accuracy landed within 1-2 percentage points of enterprise tools on standard business documents. The gap widened on edge cases — low-quality scans and handwritten content — but these documents challenged all three platforms.
Invoice extraction correctly identified key fields 93% of the time. Table structure preservation was competitive with Textract.
The Good
The Not-So-Good
Best For
Developers and teams needing quick results, businesses processing hundreds to thousands of documents monthly, anyone evaluating extraction before enterprise commitment, and users who value simplicity over maximum customization.
Head-to-Head Comparison
| Feature | Amazon Textract | Google Document AI | PDF Parser |
|---|---|---|---|
| Average Accuracy | 94.2% | 95.8% | 93.5% |
| Setup Time | 2-4 hours | 2-4 hours | 30 seconds |
| Account Required | AWS + billing | GCP + billing | Optional |
| Cost per 1,000 docs | $20-50 (with infra) | $35-70 (with infra) | $10-20 |
| Custom Training | Queries only | Full processor training | No |
| API Complexity | High | High | Low |
| Best For | AWS enterprises | GCP enterprises | SMBs, developers |
Real-World Accuracy by Document Type
The marketing brochures show impressive numbers. Here's what we actually measured:
Invoices (40 documents)
All three handled standard invoices well. Document AI's Invoice Parser had a slight edge on vendor name extraction.
Complex Tables (30 documents)
Nested headers and merged cells challenged every tool. Document AI's column detection was noticeably better.
Scanned Forms at 200+ DPI (15 documents)
Clean scans produced similar results across platforms. The quality of your source documents matters more than which tool you choose.
Low-Quality Scans Under 150 DPI (5 documents)
All tools struggled here. If your documents are this degraded, expect to need human review regardless of platform.
Handwritten Annotations (10 documents)
Handwriting remains hard. None of these tools are reliable for handwritten content without significant human verification.
When to Choose Each Option
Choose Amazon Textract when:
Choose Google Document AI when:
Choose PDF Parser when:
The Honest Conclusion
Amazon Textract and Google Document AI are serious tools. They've earned their place in enterprise workflows through deep platform integration, custom training capabilities, and massive scale support.
They've also earned their complexity. Setup takes hours. Pricing requires spreadsheet analysis. Getting started requires cloud platform expertise.
PDF Parser exists for the rest of us. The accuracy gap on standard business documents is smaller than the enterprise marketing suggests — typically 1-3 percentage points. The experience gap is enormous.
If you're processing millions of pages monthly and have DevOps support, enterprise tools make sense. If you're a developer who needs to parse documents without cloud certifications, a business user who wants results in minutes, or a team evaluating extraction before signing contracts — simpler wins.
Test PDF Parser with your own documents — 100 free credits, no account required.
