AI-Powered OCR Correction: Making Legal Documents More Readable
AI-Powered OCR Correction: Making Legal Documents More Readable
We've just implemented two major features that dramatically improve the quality and readability of OCR text in legal documents. These features work together to automatically detect poor OCR quality and use AI to correct common transcription errors while preserving the legal accuracy of the original documents.
What We've Built
🤖 LLM Correction Pass
The Problem: OCR engines often produce garbled text, especially with legal documents that have complex formatting, signatures, stamps, and handwritten annotations.
The Solution: AI-powered correction using large language models specifically trained to understand legal document context.
Key Features:
- Contextual corrections using GPT-4 and Claude models
- Legal document optimization with specialized prompts
- Side-by-side storage of original vs corrected text
- Confidence scoring for each correction (1-100 scale)
- Cost control with daily API limits and rate limiting
- Idempotent processing - safe to re-run without duplicating work
Technical Implementation:
# Two-round correction process
corrected_text = llm_client.correct_ocr_text(original_text, "Legal Document")
assessment = llm_client.assess_correction_quality(original_text, corrected_text)
Example Transformation:
Original OCR: "The defendent waives his right to councel and agrees to proceed pro se."
Corrected: "The defendant waives his right to counsel and agrees to proceed pro se."
🔍 Error Detection & Quality Assessment
The Problem: Many pages have completely garbled OCR output - patterns like "0. 0 00 0" or keyboard mashing that makes documents unsearchable.
The Solution: Intelligent quality assessment that automatically detects low-quality OCR and flags pages for high-quality reprocessing.
Key Features:
- Garbage detection for nonsensical OCR patterns
- Quality scoring based on character patterns and word frequency
- Automatic flagging for reprocessing queue
- Multiple preprocessing pipelines (contrast, denoising, rotation)
- A/B testing of different OCR engines
- Batch reprocessing of flagged documents
Quality Detection Patterns:
- Too short text: Less than 10 characters
- Character repetition: "qqqq wwww eeee" patterns
- Keyboard patterns: "asdf qwer zxcv" sequences
- Excessive special chars: "!@#$%^&()" overload
- Non-alphabetic content*: Mostly symbols and numbers
How It Works
Processing Pipeline
- Quality Assessment: Each OCR result is analyzed for quality indicators
- Low-Quality Detection: Pages with poor OCR are flagged for reprocessing
- LLM Correction: High-quality OCR text gets AI correction for common errors
- Confidence Scoring: Each correction receives a quality score
- Database Storage: Both original and corrected text are stored side-by-side
- UI Integration: Users see corrected text by default with toggle to original
Safety Features
Legal Accuracy Protection:
- No content modification - only fixes OCR transcription errors
- Preserves legal terminology and proper names
- Maintains original document structure
- Flags uncertain corrections with [UNCERTAIN: reason] tags
- Never changes meaning or substance of legal text
Cost & Rate Limiting:
- Daily API cost limits (configurable, default $50/day)
- Rate limiting with graceful exit on 429 responses
- Token counting to estimate costs before processing
- Batch processing for efficiency
- Idempotent operations to prevent duplicate API calls
User Experience
Document Viewer Integration
When viewing documents with corrections:
- Corrected text displayed by default for better readability
- Confidence badge showing correction quality (e.g., "85% confidence")
- "View Original" button to toggle between corrected and original text
- Seamless switching without page reload
- Visual indicators for high-quality corrections
Search Improvements
- Better search results with corrected text making documents more findable
- Contextual excerpts show corrected text in search results
- Improved relevance as corrected text matches user queries better
- Preserved original for legal accuracy verification
Technical Architecture
Database Schema
-- OCR corrections table
CREATE TABLE ocr_corrections (
id INTEGER PRIMARY KEY,
image_id INTEGER,
original_text TEXT,
corrected_text TEXT,
quality_score INTEGER,
confidence_level TEXT,
model_used TEXT,
processing_time_ms INTEGER,
created_at TIMESTAMP
);
-- Reprocessing queue for low-quality OCR
CREATE TABLE ocr_reprocessing_queue (
id INTEGER PRIMARY KEY,
image_id INTEGER,
reprocess_reason TEXT,
priority INTEGER,
status TEXT,
created_at TIMESTAMP
);
API Integration
Supported Models:
- OpenAI GPT-4 (primary)
- Anthropic Claude-3-Sonnet (fallback)
- Configurable model selection per batch
Rate Limiting:
- Respects API limits with proper 429 handling
- Exponential backoff for temporary failures
- Daily limit detection with graceful exit
- Cost monitoring to prevent runaway expenses
Configuration & Usage
Quick Start
# Set up API keys
export OPENAI_API_KEY="your_key_here"
# Process 10 images with corrections
python llm_correction_processor.py --batch-size 10
# Check quality assessment
python helpers/ocr_quality_assessment.py
Configuration Options
# Cost control
MAX_DAILY_API_COST_USD=50.0
MAX_TOKENS_PER_REQUEST=8000
# Processing settings
BATCH_SIZE=10
MIN_OCR_TEXT_LENGTH=10
RATE_LIMIT_DELAY=1.0
# Model selection
DEFAULT_LLM_MODEL=gpt-4
FALLBACK_LLM_MODEL=claude-3-sonnet
Performance Impact
Processing Speed
- Batch processing for efficiency
- Parallel API calls where possible
- Smart caching to avoid duplicate work
- Background processing that doesn't block the UI
Cost Management
- Token estimation before API calls
- Daily cost limits with automatic shutdown
- Quality-based processing (only process if changes detected)
- Idempotent operations prevent duplicate charges
Database Optimization
- Efficient queries for correction lookups
- Indexed columns for fast searches
- Minimal storage overhead with compressed text
- Cleanup routines for old processing logs
What This Enables
For Researchers
- More accurate search results with corrected text
- Better document readability for analysis
- Preserved legal accuracy for citations
- Quality indicators to trust correction reliability
For Journalists
- Faster document review with readable text
- Better context understanding from corrected excerpts
- Original text access for verification
- Improved search capabilities for investigation
For Legal Professionals
- Accurate text for legal research
- Preserved legal terminology and citations
- Quality confidence for document reliability
- Original text preservation for legal accuracy
Quality Metrics
Correction Quality
- Confidence scoring (1-100 scale)
- Change validation (only saves if meaningful changes)
- Legal accuracy preservation (no content modification)
- Human review flags for low-confidence corrections
Processing Metrics
- Success rates by document type
- API cost tracking per correction
- Processing time per document
- Quality improvement before/after scores
Future Enhancements
Planned Features
- Document type detection for specialized prompts
- Entity recognition for better context
- Cross-reference linking between related documents
- Batch quality reporting for processing insights
- Human review workflows for low-confidence corrections
Advanced Processing
- Multi-model comparison for best results
- Custom legal prompts for specific document types
- Quality feedback loops to improve accuracy
- Automated testing for correction quality
Safety & Legal Considerations
Content Preservation
- Never modifies legal content - only fixes OCR errors
- Preserves all legal terminology and proper names
- Maintains document structure and formatting
- Flags uncertain corrections for human review
Data Integrity
- Original text always preserved alongside corrections
- Version control for all text changes
- Audit trail for correction history
- Rollback capability to original OCR text
Try It Out
View Corrected Documents
- Browse to any document in the viewer
- Look for confidence badges on corrected pages
- Click "View Original" to toggle between versions
- Notice improved readability in corrected text
Search with Corrections
- Search for terms that might have OCR errors
- See corrected text in search excerpts
- Compare original vs corrected for accuracy
- Enjoy better search relevance from corrections
Technical Files
Core Implementation:
- helpers/llm_client.py - LLM API integration
- helpers/ocr_quality_assessment.py - Quality detection
- llm_correction_processor.py - Main processing script
- llm_correction_config.py - Configuration management
Database Integration:
- app.py - UI integration and display logic
- index_images.py - Schema updates and processing
- process_reprocessing_queue.py - Queue management
Conclusion
These OCR correction features represent a major leap forward in making legal documents more accessible and searchable. By combining intelligent quality assessment with AI-powered correction, we're able to:
- Automatically detect poor OCR quality
- Correct common errors while preserving legal accuracy
- Improve search results with better text quality
- Maintain transparency with original text preservation
- Scale efficiently with cost controls and rate limiting
The result is a system that makes government documents more readable and searchable while maintaining the legal accuracy required for serious research and investigation.
This update covers the implementation of AI-powered OCR correction features, including LLM text correction and intelligent quality assessment. These features are now live and processing documents to improve readability and searchability.