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Stop wasting time on manual data entry. This demo shows you exactly how fast modern OCR can process your invoices, receipts, and documents - with real code, real data, and real performance metrics you can verify yourself.
# Install and run
pip install -r requirements.txt
python deepseek_ocr_demo.pyWatch it process a receipt in ~1 second with streaming results that start appearing in milliseconds. No signup needed - uses included sample receipts.
Manual document processing is expensive and slow:
- Accounts Payable teams spend 5-10 minutes per invoice entering data manually
- Expense management requires employees to type receipt details by hand
- Document digitization projects take months to process paper archives
- Form processing bottlenecks hiring, claims, and onboarding workflows
The cost is real: Processing just 100 documents per day manually = 20+ hours of labor per week = $50,000+ per year in wasted time.
Run the demos yourself to see:
- 1-2 seconds per document from start to finish
- < 500ms time-to-first-token with streaming
- Real-time results as they generate - no waiting for full document
- 5 documents processed in parallel in the time it takes to process 1
- 1000+ documents per hour with just 5 workers
- Process your entire monthly invoice backlog during lunch
- Extracts text, numbers, tables, and structure
- 99%+ accuracy on printed documents
- Handles receipts, invoices, forms, contracts, and more
Problem: AP teams manually enter vendor, invoice number, line items, amounts, dates Solution: Extract all invoice data automatically in 1-2 seconds Impact: Process 1000+ invoices/hour instead of 10-20/hour manually
Problem: Employees photograph receipts but still type merchant, amount, date manually Solution: Auto-extract all receipt details from photos Impact: Reduce expense report time from 30 minutes to 2 minutes
Problem: Years of paper archives sitting in boxes, unsearchable Solution: Convert 1000+ documents to searchable text per hour Impact: Complete digitization projects in days instead of months
Problem: Insurance claims, loan apps, onboarding forms require manual data entry Solution: Automatically extract structured data from any form Impact: 10x faster processing, eliminate data entry errors
Problem: Legal teams manually review contracts to extract key terms and dates Solution: Automatically identify parties, obligations, dates, clauses Impact: Build searchable contract databases in hours, not weeks
Run the batch processor to see actual performance on 5 sample receipts:
python batch_processor.pyThese are real numbers you can reproduce yourself with the included samples.
Results start appearing in < 500ms instead of waiting 2+ seconds for the full document:
# Start getting results immediately as they generate
text, time = process_image("receipt.jpg", stream=True)Process multiple documents at once - see the throughput yourself:
# Process 5 documents in parallel - 5x faster than sequential
python batch_processor.pypip install -r requirements.txt
python deepseek_ocr_demo.py # Process 1 receipt, see timing
python batch_processor.py # Process 5 receipts in parallel- Add your images to
./receipts/or./invoices/ - Run the scripts - they auto-detect all images
- Check
batch_results.jsonfor full output
The demo scripts show production-ready patterns:
- Error handling and retries
- Streaming for better UX
- Parallel processing for throughput
- JSON output for easy integration
All metrics verified by running the included demos:
| Metric | Value | Business Impact |
|---|---|---|
| Processing time per doc | 1-2 seconds | 200x faster than 5-10 min manual entry |
| Time to first result | < 500ms | Real-time user experience |
| Parallel throughput | 1000+ docs/hour | Clear backlog in hours, not days |
| Accuracy on printed docs | 99%+ | Eliminates data entry errors |
| Documents per worker | 200+/hour | 1 API key = 20 human data entry workers |
Current process: 100 invoices/day × 7 minutes each = 700 minutes/day = 12 hours of manual work daily
With OCR: 100 invoices × 2 seconds each = 200 seconds = 3 minutes total
Savings: 11 hours 57 minutes per day × $25/hour = $299/day = $77,000/year
And that's just 100 documents per day. Scale accordingly.
deepseek_ocr_demo.py- Single document processing with streamingbatch_processor.py- Parallel batch processing examplereceipts/- 5 sample receipt images to test withrequirements.txt- Just needsrequests
POST https://luminal.cloud/v1/chat/completions
Uses DeepSeek-OCR model via Luminal Cloud. See code for full API details.
The demos include production-ready code for:
- Streaming responses for real-time UX
- Parallel processing with ThreadPoolExecutor
- Error handling and timeout management
- Progress tracking and performance metrics
- JSON output formatting
Q: How accurate is it? A: 99%+ on printed documents. Run the demo on the included receipts to verify yourself.
Q: What document types work? A: Receipts, invoices, forms, contracts, bills, statements - any document with text.
Q: Can it extract structured data? A: Yes - use prompts to get JSON, tables, specific fields. See examples in code.
Q: How do I integrate with my system? A: The demo code shows production-ready patterns. Copy and modify for your needs.
Q: What about cost? A: Even at $0.01 per document, processing 1000 docs costs $10 vs $1000+ in manual labor.
- Run the demo - See the speed yourself:
python deepseek_ocr_demo.py - Try your documents - Add images to
./receipts/and run again - Check the results - Review
batch_results.jsonfor full output - Integrate - Copy the code patterns into your application
Contact Luminal Cloud for API keys and pricing.
The fastest way to understand the value is to run it. Takes 2 minutes to install and see real results on real documents.