from paddleocr import PaddleOCR import re # Initialize OCR ocr = PaddleOCR(use_angle_cls=True, lang='en') # Helper regex patterns PAN_REGEX = r'\b[A-Z]{5}[0-9]{4}[A-Z]\b' AADHAAR_REGEX = r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}\b' DOB_REGEX = [ r'\b\d{2}[./-]\d{2}[./-]\d{4}\b', # 12/04/1980 or 12-04-1980 r'\b\d{4}-\d{2}-\d{2}\b', # 1980-04-12 r'\b\d{2}[./-](JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)[./-]\d{4}\b', # 12-APR-1980 r'\b(19|20)\d{2}\b' # Year only ] GENDERS = ["MALE", "FEMALE", "TRANSGENDER"] def extract_kyc_fields(file_path, force_type=None): try: result = ocr.ocr(file_path, cls=True) lines = [] # Normalize OCR text for block in result: for line in block: text = re.sub(r'\s+', ' ', line[1][0].strip()) if text: lines.append(text) full_text = "\n".join(lines) # Determine card type if force_type: card_type = force_type.upper() else: pan_match = re.search(PAN_REGEX, full_text) aadhaar_match = re.search(AADHAAR_REGEX, full_text) card_type = "UNKNOWN" if pan_match: card_type = "PAN" elif aadhaar_match: card_type = "AADHAAR" response = {"card_type": card_type} if card_type == "PAN": pan_number = re.search(PAN_REGEX, full_text) response["pan_number"] = pan_number.group(0) if pan_number else "Not found" response["dob"] = extract_dob(lines) response["name"] = extract_pan_name(lines) elif card_type == "AADHAAR": aadhaar_number = re.search(AADHAAR_REGEX, full_text) response["aadhaar_number"] = aadhaar_number.group(0) if aadhaar_number else "Not found" response["dob"] = extract_dob(lines) response["gender"] = extract_gender(lines) response["name"] = extract_aadhaar_name(lines) else: response["error"] = "Could not identify document as PAN or Aadhaar." return response except Exception as e: return {"error": f"OCR processing failed: {str(e)}"} def extract_dob(lines): for line in lines: for pattern in DOB_REGEX: match = re.search(pattern, line, re.IGNORECASE) if match: return match.group(0) return "Not found" def extract_gender(lines): for line in lines: for gender in GENDERS: if gender in line.upper(): return gender return "Not found" def extract_pan_name(lines): # Heuristic: Name is after "INCOME TAX DEPARTMENT" and contains only letters/spaces for i, line in enumerate(lines): if "INCOME TAX DEPARTMENT" in line.upper(): for j in range(i + 1, len(lines)): candidate = lines[j].strip() if re.match(r'^[A-Z\s.]+$', candidate) and not re.search(r'\d', candidate): # Skip words like INDIA, GOVT, DEPARTMENT if not any(x in candidate.upper() for x in ["INDIA", "GOVT", "DEPARTMENT"]): return candidate return "Not found" def extract_aadhaar_name(lines): # Heuristic: Name is usually above DOB for i, line in enumerate(lines): if any(re.search(p, line) for p in DOB_REGEX): if i > 0: candidate_name = lines[i - 1].strip() if not re.search(r'\d', candidate_name) and len(candidate_name.split()) >= 2: if not any(x in candidate_name.upper() for x in ["DOB", "INDIA", "MALE", "FEMALE", "GOVERNMENT"]): return candidate_name # Fallback: First line with >=2 words and no digits for line in lines: candidate_name = line.strip() if len(candidate_name.split()) >= 2 and not re.search(r'\d', candidate_name): if not any(x in candidate_name.upper() for x in ["DOB", "INDIA", "MALE", "FEMALE", "GOVERNMENT"]): return candidate_name return "Not found"