Marius Mutu
495790411f
Add docTR as primary OCR engine with 2-tier sequential processing, OCR metrics tracking, and simplified engine selection. Features: - docTR OCR engine with light+medium preprocessing tiers - doctr_plus mode with early exit optimization (~65% fast path) - OCR metrics dashboard with per-engine statistics - User OCR preference persistence - Parallel worker pool for OCR processing - Cross-validation for extraction quality Engine options: tesseract, doctr, doctr_plus (recommended), paddleocr 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
594 lines
24 KiB
Python
594 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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OCR Direct Validation Tests
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This script validates the OCR extraction accuracy by:
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1. Generating a test JWT token
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2. Calling the OCR API endpoint with PDF receipts
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3. Comparing extracted data with expected values from expected_receipts.json
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Run:
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python tests/ocr-validation/ocr-direct-validation.py
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python tests/ocr-validation/ocr-direct-validation.py --engine doctr_plus
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python tests/ocr-validation/ocr-direct-validation.py --receipt receipt_01
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"""
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import sys
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import os
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import json
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import argparse
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import time
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import requests
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from pathlib import Path
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from datetime import datetime, timedelta
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from typing import Optional, Dict, List, Any
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# Add backend and project root to path
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project_root = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(project_root))
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sys.path.insert(0, str(project_root / 'backend'))
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# Import JWT handler to create test tokens
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from jose import jwt
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def create_test_token(secret_key: str) -> str:
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"""Create a test JWT token for API authentication."""
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now = datetime.utcnow()
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expire = now + timedelta(hours=1)
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payload = {
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"username": "ocr_test_user",
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"user_id": 999,
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"companies": ["TEST"],
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"permissions": ["read", "write"],
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"exp": expire,
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"iat": now,
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"type": "access"
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}
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return jwt.encode(payload, secret_key, algorithm="HS256")
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def normalize_cui(cui: Optional[str]) -> Optional[str]:
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"""Normalize CUI by removing RO prefix and spaces."""
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if not cui:
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return None
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return cui.upper().replace('RO', '').replace(' ', '')
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def normalize_date(date: Optional[str]) -> Optional[str]:
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"""Normalize date to YYYY-MM-DD format."""
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if not date:
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return None
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try:
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# Try parsing ISO format
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from datetime import datetime
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parsed = datetime.fromisoformat(date.replace('Z', '+00:00'))
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return parsed.strftime('%Y-%m-%d')
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except:
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return date
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def compare_with_tolerance(expected: float, actual, tolerance: float) -> bool:
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"""Compare numbers with tolerance."""
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if actual is None:
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return False
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# Handle string values from API
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try:
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actual_float = float(actual)
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except (ValueError, TypeError):
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return False
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diff = abs(expected - actual_float)
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threshold = expected * tolerance
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return diff <= threshold or diff <= 0.01 # Within tolerance or 1 cent
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def submit_ocr_job(api_base: str, token: str, pdf_path: Path, engine: str) -> dict:
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"""Submit a PDF file for OCR processing and wait for result.
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Returns dict with detailed timing information:
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- timing.submit_duration: time to submit job and get job_id
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- timing.poll_count: number of poll requests made
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- timing.poll_duration: total time spent polling
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- timing.wall_time: total elapsed time (submit + poll)
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- timing.api_reported_ms: processing time reported by API
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"""
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headers = {'Authorization': f'Bearer {token}'}
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# Timing tracking
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timing = {
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'submit_duration': 0.0,
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'poll_count': 0,
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'poll_duration': 0.0,
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'wall_time': 0.0,
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'api_reported_ms': 0,
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}
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wall_start = time.time()
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# Submit job
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submit_start = time.time()
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with open(pdf_path, 'rb') as f:
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files = {'file': (pdf_path.name, f, 'application/pdf')}
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response = requests.post(
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f'{api_base}/api/data-entry/ocr/extract?engine={engine}',
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headers=headers,
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files=files,
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timeout=60
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)
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timing['submit_duration'] = time.time() - submit_start
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if not response.ok:
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timing['wall_time'] = time.time() - wall_start
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return {'status': 'failed', 'error': f'Submit failed: {response.status_code} - {response.text}', 'timing': timing}
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job = response.json()
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job_id = job.get('job_id')
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if not job_id:
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timing['wall_time'] = time.time() - wall_start
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return {'status': 'failed', 'error': 'No job_id in response', 'timing': timing}
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# Poll for result
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poll_start = time.time()
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max_wait = 120 # 2 minutes
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while time.time() - wall_start < max_wait:
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timing['poll_count'] += 1
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poll_response = requests.get(
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f'{api_base}/api/data-entry/ocr/jobs/{job_id}/wait?timeout=30',
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headers=headers,
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timeout=35
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)
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if not poll_response.ok:
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time.sleep(1)
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continue
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job_status = poll_response.json()
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if job_status.get('status') == 'completed':
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timing['poll_duration'] = time.time() - poll_start
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timing['wall_time'] = time.time() - wall_start
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# Detailed timing from API
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timing['queue_wait_ms'] = job_status.get('queue_wait_ms', 0) or 0
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timing['ocr_time_ms'] = job_status.get('ocr_time_ms', 0) or 0
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timing['processing_time_ms'] = job_status.get('processing_time_ms', 0) or 0
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return {
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'status': 'completed',
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'result': job_status.get('result', {}),
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'processing_time_ms': job_status.get('processing_time_ms', 0),
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'timing': timing
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}
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if job_status.get('status') == 'failed':
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timing['poll_duration'] = time.time() - poll_start
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timing['wall_time'] = time.time() - wall_start
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return {'status': 'failed', 'error': job_status.get('error', 'Unknown error'), 'timing': timing}
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# Still pending - show status but don't spam
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if timing['poll_count'] <= 3 or timing['poll_count'] % 5 == 0:
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print(f" Status: {job_status.get('status')}, position: {job_status.get('queue_position')}, polls: {timing['poll_count']}")
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timing['poll_duration'] = time.time() - poll_start
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timing['wall_time'] = time.time() - wall_start
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return {'status': 'failed', 'error': 'Timeout waiting for OCR result', 'timing': timing}
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def main():
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parser = argparse.ArgumentParser(description='OCR Direct Validation')
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parser.add_argument('--engine', default='doctr_plus',
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choices=['tesseract', 'doctr', 'doctr_plus', 'paddleocr'],
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help='OCR engine to use (doctr_plus recommended)')
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parser.add_argument('--receipt', help='Specific receipt ID to test (e.g., receipt_01)')
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parser.add_argument('--verbose', '-v', action='store_true', help='Verbose output')
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parser.add_argument('--api-base', default='http://localhost:8000', help='API base URL')
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parser.add_argument('--stop-on-issue', action='store_true',
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help='Stop at first receipt with wall_time > 7.5s or extraction errors')
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parser.add_argument('--include-multipage', action='store_true',
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help='Include multi-page PDFs (normally skipped)')
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args = parser.parse_args()
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# Paths
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script_dir = Path(__file__).parent
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expected_path = script_dir / 'expected_receipts.json'
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pdf_base_path = script_dir.parent.parent / 'docs' / 'data-entry'
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# JWT secret from environment or default
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jwt_secret = os.getenv('JWT_SECRET_KEY', 'generate_with_secrets_token_urlsafe_32')
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# Create test token
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token = create_test_token(jwt_secret)
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# Load expected data
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print(f"\n{'='*60}")
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print("OCR API VALIDATION")
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print(f"{'='*60}")
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print(f"Engine: {args.engine}")
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print(f"API Base: {args.api_base}")
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print(f"Expected data: {expected_path}")
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print(f"PDF folder: {pdf_base_path}")
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with open(expected_path) as f:
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expected_data = json.load(f)
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# Filter receipts
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receipts_to_test = expected_data['receipts']
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# Skip multi-page PDFs unless explicitly included
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if not args.include_multipage:
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original_count = len(receipts_to_test)
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receipts_to_test = [r for r in receipts_to_test if r.get('page') is None]
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skipped = original_count - len(receipts_to_test)
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if skipped > 0:
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print(f"Skipping {skipped} multi-page PDF entries (use --include-multipage to include)")
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# Filter by specific receipt ID if requested
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if args.receipt:
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receipts_to_test = [r for r in receipts_to_test if r['id'] == args.receipt]
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if not receipts_to_test:
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print(f"\nError: Receipt ID '{args.receipt}' not found")
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sys.exit(1)
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print(f"Receipts to test: {len(receipts_to_test)}")
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print(f"{'='*60}\n")
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# Results storage
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results: List[Dict[str, Any]] = []
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# Test each receipt
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for expected in receipts_to_test:
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pdf_path = pdf_base_path / expected['filename']
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if not pdf_path.exists():
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print(f"[SKIP] File not found: {expected['filename']}")
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continue
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print(f"[TEST] Processing: {expected['filename']}")
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try:
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# Submit OCR job via API
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start_time = datetime.now()
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ocr_result = submit_ocr_job(args.api_base, token, pdf_path, args.engine)
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# Handle processing_time which may be string or number
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raw_time = ocr_result.get('processing_time_ms')
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if raw_time is not None:
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processing_time = float(raw_time)
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else:
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processing_time = (datetime.now() - start_time).total_seconds() * 1000
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if ocr_result.get('status') == 'failed':
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print(f" [ERROR] OCR failed: {ocr_result.get('error')}")
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results.append({
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'receipt_id': expected['id'],
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'filename': expected['filename'],
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'status': 'failed',
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'error': ocr_result.get('error'),
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})
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continue
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# Get extracted values
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extracted = ocr_result.get('result', {})
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# Safe float conversion helper
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def safe_float(value, default=0.0):
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if value is None:
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return default
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try:
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return float(value)
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except (ValueError, TypeError):
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return default
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# Compare results
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comparison = {
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'receipt_id': expected['id'],
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'filename': expected['filename'],
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'status': 'completed',
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'total_expected': expected['total'],
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'total_extracted': safe_float(extracted.get('amount'), None),
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'total_match': False,
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'date_expected': expected['data_bon'],
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'date_extracted': extracted.get('receipt_date'),
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'date_match': False,
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'cui_expected': expected['cui_furnizor'],
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'cui_extracted': extracted.get('cui'),
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'cui_match': False,
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'tva_expected': expected['total_tva'],
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'tva_extracted': safe_float(extracted.get('tva_total'), None),
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'tva_match': False,
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'confidence': safe_float(extracted.get('overall_confidence'), 0),
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'processing_time_ms': processing_time,
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'ocr_engine': extracted.get('ocr_engine', args.engine),
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'errors': [],
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# NEW: Save full extraction for analysis
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'full_extraction': {
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'amount': extracted.get('amount'),
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'receipt_date': extracted.get('receipt_date'),
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'cui': extracted.get('cui'),
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'tva_total': extracted.get('tva_total'),
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'tva_entries': extracted.get('tva_entries', []),
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'supplier_name': extracted.get('supplier_name'),
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'receipt_number': extracted.get('receipt_number'),
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'payment_methods': extracted.get('payment_methods', []),
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'items_count': extracted.get('items_count'),
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'overall_confidence': extracted.get('overall_confidence'),
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'confidence_amount': extracted.get('confidence_amount'),
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'confidence_date': extracted.get('confidence_date'),
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'confidence_cui': extracted.get('confidence_cui'),
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},
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# NEW: Save raw OCR texts from each engine pass
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'raw_texts': extracted.get('raw_texts', []),
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}
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# Compare TOTAL
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comparison['total_match'] = compare_with_tolerance(
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expected['total'],
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extracted.get('amount'),
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0.02 # 2% tolerance
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)
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if not comparison['total_match']:
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comparison['errors'].append(
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f"TOTAL: expected {expected['total']}, got {extracted.get('amount')}"
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)
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# Compare DATE
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normalized_expected_date = normalize_date(expected['data_bon'])
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normalized_extracted_date = normalize_date(extracted.get('receipt_date'))
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comparison['date_match'] = normalized_expected_date == normalized_extracted_date
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if not comparison['date_match']:
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comparison['errors'].append(
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f"DATE: expected {normalized_expected_date}, got {normalized_extracted_date}"
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)
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# Compare CUI
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normalized_expected_cui = normalize_cui(expected['cui_furnizor'])
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normalized_extracted_cui = normalize_cui(extracted.get('cui'))
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comparison['cui_match'] = normalized_expected_cui == normalized_extracted_cui
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if not comparison['cui_match']:
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comparison['errors'].append(
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f"CUI: expected {normalized_expected_cui}, got {normalized_extracted_cui}"
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)
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# Compare TVA
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if expected['total_tva'] > 0:
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comparison['tva_match'] = compare_with_tolerance(
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expected['total_tva'],
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extracted.get('tva_total'),
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0.05 # 5% tolerance
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)
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if not comparison['tva_match']:
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comparison['errors'].append(
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f"TVA: expected {expected['total_tva']}, got {extracted.get('tva_total')}"
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)
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else:
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# No TVA expected (neplatitor TVA)
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tva_extracted = safe_float(extracted.get('tva_total'), None)
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comparison['tva_match'] = tva_extracted is None or tva_extracted == 0 or tva_extracted == 0.0
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results.append(comparison)
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# Get timing info from API (detailed breakdown)
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t = ocr_result.get('timing', {})
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wall_ms = t.get('wall_time', 0) * 1000
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queue_wait_ms = t.get('queue_wait_ms', 0)
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ocr_time_ms = t.get('ocr_time_ms', 0)
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processing_time_ms = t.get('processing_time_ms', 0)
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# Overhead = wall_time - processing_time (includes network, polling)
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overhead_ms = wall_ms - processing_time_ms if processing_time_ms else 0
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# Print result
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status = 'PASS' if not comparison['errors'] else 'FAIL'
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print(f" [{status}] Total: {expected['total']} vs {extracted.get('amount')} ({comparison['total_match']})")
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print(f" Date: {expected['data_bon']} vs {extracted.get('receipt_date')} ({comparison['date_match']})")
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print(f" CUI: {expected['cui_furnizor']} vs {extracted.get('cui')} ({comparison['cui_match']})")
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print(f" TVA: {expected['total_tva']} vs {extracted.get('tva_total')} ({comparison['tva_match']})")
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print(f" Confidence: {comparison['confidence']*100:.1f}%")
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# Print detailed timing breakdown
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print(f" TIMING: ocr={ocr_time_ms}ms, queue_wait={queue_wait_ms}ms, "
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f"job_total={processing_time_ms}ms, wall={wall_ms:.0f}ms")
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print(f" overhead={overhead_ms:.0f}ms (wall - job_total)")
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if comparison['errors'] and args.verbose:
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for err in comparison['errors']:
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print(f" Error: {err}")
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# Stop on issue if requested
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if args.stop_on_issue:
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has_errors = len(comparison['errors']) > 0
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# Use OCR time for threshold (actual processing, not queue wait)
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ocr_too_slow = ocr_time_ms > 10000 # 10s threshold for actual OCR
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if has_errors or ocr_too_slow:
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print(f"\n{'='*60}")
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print(f"STOP: Issue detected on {expected['filename']}")
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print(f"{'='*60}")
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if ocr_too_slow:
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print(f" SLOW: ocr_time={ocr_time_ms}ms > 10000ms threshold")
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if has_errors:
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print(f" ERRORS: {comparison['errors']}")
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print(f"\n Full timing breakdown:")
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print(f" ocr_time_ms: {ocr_time_ms}ms (actual OCR engine time)")
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print(f" queue_wait_ms: {queue_wait_ms}ms (waiting in queue)")
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print(f" processing_time_ms: {processing_time_ms}ms (job total)")
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print(f" wall_time: {wall_ms:.0f}ms (client-side)")
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print(f" overhead: {overhead_ms:.0f}ms (network + polling)")
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print(f"\n Full extraction:")
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print(json.dumps(extracted, indent=4, default=str))
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sys.exit(1)
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except Exception as e:
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import traceback
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print(f" [ERROR] {str(e)}")
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if args.verbose:
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traceback.print_exc()
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results.append({
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'receipt_id': expected['id'],
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'filename': expected['filename'],
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'status': 'error',
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'error': str(e),
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})
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# Calculate statistics
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completed_results = [r for r in results if r.get('status') == 'completed']
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total_tests = len(completed_results)
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if total_tests == 0:
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print("\nNo tests completed successfully!")
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sys.exit(1)
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perfect_matches = len([r for r in completed_results
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if r['total_match'] and r['date_match'] and r['cui_match'] and r['tva_match']])
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total_match_rate = len([r for r in completed_results if r['total_match']]) / total_tests
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date_match_rate = len([r for r in completed_results if r['date_match']]) / total_tests
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cui_match_rate = len([r for r in completed_results if r['cui_match']]) / total_tests
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tva_match_rate = len([r for r in completed_results if r['tva_match']]) / total_tests
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avg_confidence = sum(r['confidence'] for r in completed_results) / total_tests
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avg_processing_time = sum(r['processing_time_ms'] for r in completed_results) / total_tests
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# Print summary
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print(f"\n{'='*60}")
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print("OCR VALIDATION SUMMARY")
|
|
print(f"{'='*60}")
|
|
print(f"Total Receipts Tested: {total_tests}")
|
|
print(f"Perfect Matches: {perfect_matches} ({perfect_matches/total_tests*100:.1f}%)")
|
|
print("---")
|
|
print(f"Total Amount Match Rate: {total_match_rate*100:.1f}%")
|
|
print(f"Date Match Rate: {date_match_rate*100:.1f}%")
|
|
print(f"CUI Match Rate: {cui_match_rate*100:.1f}%")
|
|
print(f"TVA Match Rate: {tva_match_rate*100:.1f}%")
|
|
print("---")
|
|
print(f"Average Confidence: {avg_confidence*100:.1f}%")
|
|
print(f"Average Processing Time: {avg_processing_time:.0f}ms")
|
|
print(f"{'='*60}")
|
|
|
|
# Failed receipts
|
|
failed_results = [r for r in completed_results if r.get('errors')]
|
|
if failed_results:
|
|
print(f"\nFAILED RECEIPTS ({len(failed_results)}):")
|
|
for r in failed_results:
|
|
print(f" - {r['filename']}: {'; '.join(r['errors'])}")
|
|
|
|
# Categorize problems for analysis
|
|
problems_analysis = {
|
|
'cui_issues': [],
|
|
'tva_issues': [],
|
|
'total_issues': [],
|
|
'date_issues': [],
|
|
'confidence_issues': [],
|
|
}
|
|
|
|
for r in completed_results:
|
|
# CUI issues
|
|
if not r.get('cui_match'):
|
|
cui_expected = normalize_cui(r.get('cui_expected'))
|
|
cui_got = normalize_cui(r.get('cui_extracted'))
|
|
issue_type = 'missing' if not cui_got else 'mismatch'
|
|
|
|
# Check if it's a digit substitution (same length, 1-2 chars different)
|
|
if cui_expected and cui_got and len(cui_expected) == len(cui_got):
|
|
diff_count = sum(1 for a, b in zip(cui_expected, cui_got) if a != b)
|
|
if diff_count <= 2:
|
|
issue_type = 'digit_substitution'
|
|
|
|
problems_analysis['cui_issues'].append({
|
|
'file': r['filename'],
|
|
'expected': r.get('cui_expected'),
|
|
'got': r.get('cui_extracted'),
|
|
'type': issue_type,
|
|
'confidence': r.get('confidence', 0),
|
|
})
|
|
|
|
# TVA issues
|
|
if not r.get('tva_match'):
|
|
tva_expected = r.get('tva_expected', 0)
|
|
tva_got = r.get('tva_extracted')
|
|
issue_type = 'missing' if tva_got is None else 'mismatch'
|
|
|
|
# Check for 5% rate (books)
|
|
if tva_expected and tva_expected > 0:
|
|
full_ext = r.get('full_extraction', {})
|
|
total = full_ext.get('amount')
|
|
if total and tva_expected:
|
|
try:
|
|
implied_rate = float(tva_expected) / float(total) * 100
|
|
if 4 <= implied_rate <= 6:
|
|
issue_type = 'low_rate_5pct'
|
|
except:
|
|
pass
|
|
|
|
problems_analysis['tva_issues'].append({
|
|
'file': r['filename'],
|
|
'expected': tva_expected,
|
|
'got': tva_got,
|
|
'type': issue_type,
|
|
'tva_entries': r.get('full_extraction', {}).get('tva_entries', []),
|
|
})
|
|
|
|
# TOTAL issues
|
|
if not r.get('total_match'):
|
|
problems_analysis['total_issues'].append({
|
|
'file': r['filename'],
|
|
'expected': r.get('total_expected'),
|
|
'got': r.get('total_extracted'),
|
|
'confidence': r.get('confidence', 0),
|
|
'payment_methods': r.get('full_extraction', {}).get('payment_methods', []),
|
|
})
|
|
|
|
# DATE issues
|
|
if not r.get('date_match'):
|
|
problems_analysis['date_issues'].append({
|
|
'file': r['filename'],
|
|
'expected': r.get('date_expected'),
|
|
'got': r.get('date_extracted'),
|
|
})
|
|
|
|
# Low confidence issues
|
|
if r.get('confidence', 0) < 0.7:
|
|
problems_analysis['confidence_issues'].append({
|
|
'file': r['filename'],
|
|
'confidence': r.get('confidence', 0),
|
|
'errors': r.get('errors', []),
|
|
})
|
|
|
|
# Save detailed report
|
|
report = {
|
|
'test_date': datetime.now().isoformat(),
|
|
'engine': args.engine,
|
|
'statistics': {
|
|
'total_tests': total_tests,
|
|
'perfect_matches': perfect_matches,
|
|
'perfect_match_rate': perfect_matches / total_tests,
|
|
'total_match_rate': total_match_rate,
|
|
'date_match_rate': date_match_rate,
|
|
'cui_match_rate': cui_match_rate,
|
|
'tva_match_rate': tva_match_rate,
|
|
'avg_confidence': avg_confidence,
|
|
'avg_processing_time_ms': avg_processing_time,
|
|
},
|
|
'problems_analysis': problems_analysis,
|
|
'failed_receipts': [
|
|
{'filename': r['filename'], 'errors': r['errors']}
|
|
for r in failed_results
|
|
],
|
|
'detailed_results': results,
|
|
}
|
|
|
|
# Save report with engine name in filename
|
|
report_path = script_dir / f'ocr_report_{args.engine.replace("-", "_")}_FULL.json'
|
|
with open(report_path, 'w') as f:
|
|
json.dump(report, f, indent=2, default=str)
|
|
print(f"\nReport saved to: {report_path}")
|
|
|
|
# Exit with error if match rates are below threshold
|
|
if total_match_rate < 0.8:
|
|
print(f"\n[FAIL] Total match rate {total_match_rate*100:.1f}% is below 80% threshold")
|
|
sys.exit(1)
|
|
|
|
print("\n[PASS] OCR validation completed successfully!")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|