Optical Character Recognition (OCR) is a critical component of document processing in KuhstomDiscovery. This guide covers advanced configuration options and optimization techniques.

Our platform supports multiple OCR engines:

• Tesseract: Open-source engine, good for standard documents
• Azure Cognitive Services: Cloud-based, excellent accuracy
• Amazon Textract: AWS-based, handles complex layouts
• Google Cloud Vision: Advanced AI-powered recognition

Choose the appropriate OCR engine based on:

• Document type and quality
• Language requirements
• Processing speed needs
• Accuracy requirements

Configure language detection:

• Primary language selection
• Secondary language support
• Custom language models
• Mixed-language document handling

Optimize image quality before OCR:

• Deskewing: Correct document rotation
• Noise Reduction: Remove artifacts and speckles
• Contrast Enhancement: Improve text clarity
• Resolution Adjustment: Scale for optimal processing

Set minimum confidence levels for:

• Character recognition (default: 85%)
• Word recognition (default: 90%)
• Line recognition (default: 95%)

Configure document structure detection:

• Column detection
• Table recognition
• Header/footer identification
• Image/text separation

Train custom OCR models for:

• Specialized document types
• Industry-specific terminology
• Handwritten text recognition
• Low-quality documents

Before processing, evaluate:

• Image resolution (minimum 300 DPI recommended)
• Color vs. grayscale conversion
• File format optimization
• Compression settings

For large document sets:

• Parallel processing configuration
• Memory allocation settings
• Error handling preferences
• Progress monitoring options

Optimize processing speed:

• CPU core allocation
• Memory usage limits
• Network bandwidth consideration
• Queue management

Monitor OCR quality through:

• Random sampling review
• Confidence score analysis
• Error pattern identification
• Manual verification workflows

Configure responses to:

• Low confidence results
• Processing failures
• Timeout errors
• Format incompatibilities

Set up OCR API access:

• Authentication credentials
• Rate limiting settings
• Webhook notifications
• Error response handling

Connect OCR to:

• Document classification pipelines
• Review assignment systems
• Quality control processes
• Analytics and reporting

• Poor Recognition: Check image quality and preprocessing
• Slow Processing: Optimize batch settings and resources
• Language Errors: Verify language configuration
• Format Problems: Review supported file types

Track key metrics:

• Processing speed (pages per minute)
• Accuracy rates by document type
• Error frequencies
• Resource utilization

• Test different engines with sample documents
• Implement quality control workflows
• Monitor processing performance regularly
• Keep OCR engines updated
• Maintain processing logs for troubleshooting

• Custom preprocessing scripts
• Multi-engine consensus processing
• Real-time processing monitoring
• Automated quality reporting