Extractly
Technology Comparison

Extractly vs. Traditional OCR

Discover why AI-powered document extraction far surpasses traditional OCR technology. It's not just about reading text—it's about understanding documents.

40%
Higher Accuracy
90%
Less Setup Time
Zero
Manual Cleanup

The Fundamental Difference

While traditional OCR simply converts images to text, Extractly's AI understands document context, structure, and meaning to extract precise business data.

Traditional OCR Technology
Optical Character Recognition converts images to text

How it Works:

OCR identifies individual characters and words from document images, converting them to searchable text without understanding context or meaning.

Limitations:

No understanding of document structure
Poor handling of complex layouts
No data validation or field identification
Requires extensive post-processing
Extractly AI Technology
AI-powered document understanding and extraction

How it Works:

Combines Google Cloud Document AI for layout understanding with Anthropic Claude for intelligent interpretation, extracting structured business data.

Advantages:

Understands document context and meaning
Handles complex layouts, tables, and forms
Built-in validation and confidence scoring
Ready-to-use structured output

Feature-by-Feature Comparison

See how Extractly's AI-powered approach compares to traditional OCR across key capabilities.

Document Understanding

Traditional OCR
Text recognition only
Extractly AI
Context-aware AI interpretation

Complex Layouts

Traditional OCR
Struggles with tables, forms
Extractly AI
Handles any document structure

Data Validation

Traditional OCR
No validation capabilities
Extractly AI
Built-in validation & confidence scoring

Setup Complexity

Traditional OCR
Extensive configuration required
Extractly AI
Template-based, no configuration

Accuracy Rate

Traditional OCR
70-85% for structured data
Extractly AI
99.5% for all document types

Post-Processing

Traditional OCR
Extensive manual cleanup required
Extractly AI
Ready-to-use structured output

Real-World Examples

See how the two approaches handle common document processing scenarios.

Invoice Processing
Traditional OCR
Extracts individual text elements but can't identify which text represents vendor name, invoice number, or line items. Requires manual mapping and validation.
Extractly AI
Automatically identifies and extracts vendor details, invoice number, dates, line items with prices, and calculates totals—all ready for ERP integration.
Contract Analysis
Traditional OCR
Converts contract text but provides no understanding of legal clauses, parties, terms, or key dates. Manual review required for all extractions.
Extractly AI
Identifies parties, extracts key terms, dates, financial obligations, and termination clauses with legal context understanding.
Financial Statements
Traditional OCR
Struggles with financial tables and can't distinguish between different financial metrics or understand the relationship between numbers.
Extractly AI
Understands financial statement structure, extracts specific metrics like revenue, EBITDA, assets, and maintains relationships between data points.

Implementation & Maintenance

Traditional OCR Setup
1
Configure OCR Engine
Set up image preprocessing, character recognition models
2
Build Extraction Rules
Create complex regex patterns and positioning rules
3
Develop Post-Processing
Build validation logic and error correction systems
4
Ongoing Maintenance
Constant rule updates as document formats change
6+ months
Typical implementation time
Extractly AI Setup
1
Create Template
Define what data you need with simple field definitions
2
Test & Refine
Upload sample documents and refine field descriptions
3
Go Live
Start processing documents via API or dashboard
4
Zero Maintenance
AI adapts automatically to document variations
1-2 days
Typical implementation time

Ready to Move Beyond Traditional OCR?

Experience the power of AI-driven document understanding. Start with a free trial and see why thousands of businesses choose Extractly over traditional OCR.

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