28f259cd05
Major fixes to OCR store profiles for Romanian receipt extraction: - Fix ProfileRegistry module path resolution (was loading 0 profiles) - Add multiline TVA extraction for Brick, Electrobering, Gama Ink - Add "CARTE CREDIT" payment detection for OMV/SOCAR gas stations - Handle OCR artifacts: TVA→TUA, "-"→"4", I→L in CUI markers - Add client CUI patterns for Brick receipts - Add profile selection logging to ocr_extractor.py - Create test script for all 29 PDFs (test_all_profiles.py) Test results: 13/29 passing (improved from 9/29) Remaining failures are primarily OCR quality issues. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
237 lines
8.1 KiB
Python
237 lines
8.1 KiB
Python
"""
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OMV Petrom store profile for OCR extraction.
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OMV receipts typically include client CUI and use standard TVA format.
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Common at gas stations with fuel purchases.
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Date format: YYYY. MM. DD with spaces (e.g., "2025. 08. 14")
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OCR quirk: Numbers often have spaces before decimals (e.g., "55, 22" instead of "55,22")
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"""
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import re
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from datetime import date
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from decimal import Decimal, InvalidOperation
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from typing import List, Dict, Any, Tuple, Optional
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from .base import BaseStoreProfile
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from . import ProfileRegistry
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@ProfileRegistry.register
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class OMVProfile(BaseStoreProfile):
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"""
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OMV PETROM MARKETING S.R.L. - standard TVA with client CUI.
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Key characteristics:
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- Standard TVA format (usually single rate, any percentage)
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- Includes client CUI on receipt (for business purchases)
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- TVA table format: "A-XX, XX% base_amount tva_amount" (with OCR spaces)
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- Supports historical rates (19%) and current rates (21%)
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- Date format: YYYY. MM. DD (with spaces)
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- Client CUI format: "CLIENT C.U. I./C.I.F.: ROXXXXXXX"
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"""
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CUI_LIST = ["11201891"]
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NAME_PATTERNS = ["OMV", "PETROM", "OMV PETROM", "0MV"] # OCR variants
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STORE_NAME = "OMV PETROM MARKETING S.R.L."
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# OMV TVA table patterns (handles OCR spaces in numbers)
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# Format: "A-21, 00% 55, 22 318, 16" (rate, TVA amount, total)
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TVA_TABLE_PATTERNS = [
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# "A-21, 00% 55, 22 318, 16" - with spaces in numbers
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r'([A-D])\s*[-:]\s*(\d{1,2})[.,\s]*\d{0,2}\s*%\s+([\d.,\s]+)\s+([\d.,\s]+)',
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# "TOTAL TAXE: 55, 22" - fallback to TOTAL TAXE
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r'TOTAL\s+TAXE\s*:?\s*([\d.,\s]+)',
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]
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# Standard TVA pattern fallback
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TVA_STANDARD_PATTERN = r'TVA\s*:?\s*([\d.,]+)'
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# OMV specific: prioritize YYYY. MM. DD format with spaces
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DATE_PATTERNS_OCR_SPACES = [
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# YYYY. MM. DD with time (OMV format)
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(r'(\d{4})[.,]\s*(\d{2})[.,]\s*(\d{2})\s+\d{2}:\d{2}', 0.98, 'ymd'),
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(r'(\d{4})[.,]\s*(\d{2})[.,]\s*(\d{2})', 0.95, 'ymd'),
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# Fallback to DD. MM. YYYY
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(r'(\d{2})[.,]\s*(\d{2})[.,]\s*(\d{4})\s+\d{2}:\d{2}', 0.92, 'dmy'),
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(r'(\d{2})[.,]\s*(\d{2})[.,]\s*(\d{4})', 0.85, 'dmy'),
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]
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# Client CUI patterns for OMV (unique format)
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CLIENT_CUI_PATTERNS = [
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# "CLIENT C.U. I./C.I.F.: RO1879855"
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(r'CLIENT\s+C\.?\s*U\.?\s*I\.?\s*/?\s*C\.?\s*I\.?\s*F\.?\s*:?\s*(R?O?\s*\d{6,10})', 0.99),
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# "C.U.I./C.I.F. CLIENT: XXXXXXX"
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(r'C\.?\s*U\.?\s*I\.?\s*/?\s*C\.?\s*I\.?\s*F\.?\s+CLIENT\s*:?\s*(R?O?\s*\d{6,10})', 0.98),
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# Fallback to simpler pattern
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(r'CLIENT\s*:?\s*(R?O?\s*\d{6,10})', 0.90),
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]
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# Client markers for OMV
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CLIENT_MARKERS = [
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r'CLIENT\s+C\.?\s*U\.?\s*I',
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r'CLIENT\s+C\.?\s*I\.?\s*F',
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r'NUME\s+CLIENT',
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r'CLIENT\s*:',
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]
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def _clean_ocr_number(self, value: str) -> str:
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"""Remove OCR spaces from numbers (e.g., '55, 22' -> '55,22')."""
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# Remove spaces around commas and periods
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value = re.sub(r'\s*([.,])\s*', r'\1', value)
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# Remove any remaining spaces
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value = value.replace(' ', '')
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return value
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def extract_tva_entries(self, text: str) -> List[dict]:
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"""
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Extract OMV-specific TVA entries.
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OMV receipts show TVA in table format with spaces in numbers.
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Format: "A-21, 00% 55, 22 318, 16" (rate, TVA amount, base)
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Args:
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text: Raw OCR text from receipt
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Returns:
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List of TVA entries with code, percent, and amount
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"""
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entries = []
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text_upper = text.upper()
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# Try table format first: "A-21, 00% 55, 22 318, 16"
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table_pattern = self.TVA_TABLE_PATTERNS[0]
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for match in re.finditer(table_pattern, text_upper):
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try:
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code = match.group(1).upper()
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percent = int(match.group(2))
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# Clean OCR spaces from amounts
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tva_amount_str = self._clean_ocr_number(match.group(3))
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tva_amount = self._parse_decimal(tva_amount_str)
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if tva_amount and tva_amount > 0:
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entries.append({
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'code': code,
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'percent': percent,
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'amount': tva_amount
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})
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return entries # OMV usually has single TVA rate
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except (ValueError, InvalidOperation, IndexError):
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continue
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# Fallback: "TOTAL TAXE: 55, 22"
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fallback_pattern = self.TVA_TABLE_PATTERNS[1]
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match = re.search(fallback_pattern, text_upper)
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if match:
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try:
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tva_amount_str = self._clean_ocr_number(match.group(1))
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tva_amount = self._parse_decimal(tva_amount_str)
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if tva_amount and tva_amount > 0:
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entries.append({
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'code': 'A',
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'percent': 19, # Standard rate, will be corrected by validation
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'amount': tva_amount
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})
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except (ValueError, InvalidOperation):
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pass
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return entries
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def extract_client_cui(self, text: str) -> Tuple[Optional[str], float]:
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"""
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Extract client CUI from OMV receipt.
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OMV uses format: "CLIENT C.U. I./C.I.F.: RO1879855"
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Args:
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text: Raw OCR text from receipt
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Returns:
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Tuple of (cui, confidence) or (None, 0.0)
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"""
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text_upper = text.upper()
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# Check for OMV client markers
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has_client = any(
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re.search(marker, text_upper, re.IGNORECASE)
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for marker in self.CLIENT_MARKERS
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)
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if not has_client:
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return (None, 0.0)
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# Try OMV-specific patterns
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for pattern, confidence in self.CLIENT_CUI_PATTERNS:
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match = re.search(pattern, text_upper, re.IGNORECASE)
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if match:
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cui = match.group(1)
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# Clean up: remove RO prefix, spaces
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cui_digits = re.sub(r'[^0-9]', '', cui)
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if 6 <= len(cui_digits) <= 10:
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return (cui_digits, confidence)
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return (None, 0.0)
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def extract_payment_methods(self, text: str) -> List[dict]:
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"""
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Extract OMV-specific payment methods.
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OMV receipts use "CARTE CREDIT" instead of "CARD".
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Payment amount equals TOTAL for gas station receipts.
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Args:
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text: Raw OCR text from receipt
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Returns:
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List of payment methods with method, amount, and confidence
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"""
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payments = []
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text_upper = text.upper()
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# Get total amount first
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total_amount, _ = self.extract_total(text)
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if not total_amount:
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return []
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# OMV payment patterns
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payment_indicators = [
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('CARTE CREDIT', 'CARD', 0.98),
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('CARTE DE CREDIT', 'CARD', 0.98),
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('CARD', 'CARD', 0.95),
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('VISA', 'CARD', 0.95),
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('MASTERCARD', 'CARD', 0.95),
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('CONTACTLESS', 'CARD', 0.90),
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('NUMERAR', 'NUMERAR', 0.95),
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('CASH', 'NUMERAR', 0.90),
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]
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for indicator, method, confidence in payment_indicators:
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if indicator in text_upper:
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payments.append({
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'method': method,
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'amount': total_amount,
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'confidence': confidence
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})
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return payments # OMV usually has single payment method
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# Fallback: If no explicit payment but has BON FISCAL, assume CARD
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if 'BON FISCAL' in text_upper:
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payments.append({
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'method': 'CARD',
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'amount': total_amount,
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'confidence': 0.70
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})
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return payments
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def get_validation_hints(self) -> Dict[str, Any]:
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"""Return OMV-specific validation hints."""
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return {
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"has_multi_rate_tva": False,
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"card_equals_total": True, # Gas station: card equals total
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"has_client_cui": True,
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"has_efactura": False,
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"is_non_vat_payer": False,
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"tva_table_format": True,
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}
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