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Ukrainian passport series/number OCR pipeline.
Full flow:
passport image -> YOLO ROI detection -> preprocessing -> CRNN OCR
Usage:
# Python API
from pipeline import PassportOCR
ocr = PassportOCR()
result = ocr("passport.jpg")
# {"series": "НР", "number": "430098", "full": "НР430098",
# "confidence": 0.97, "readable": True}
# CLI
python pipeline.py passport.jpg
python pipeline.py passport.jpg --show
"""
from __future__ import annotations
import argparse
from pathlib import Path
import cv2
import numpy as np
from src.detect import load_detector, detect_roi, crop_roi
from src.preprocess import preprocess
from src.recognize import load_recognizer, recognize, CONFIDENCE_THRESHOLD
MODELS_DIR = Path(__file__).parent / "models"
DETECTOR_PATH = MODELS_DIR / "detector.pt"
RECOGNIZER_PATH = MODELS_DIR / "recognizer.pth"
class PassportOCR:
"""
End-to-end passport series/number recognizer.
Args:
detector_path: path to YOLO .pt weights.
recognizer_path: path to CRNN .pth checkpoint.
det_conf: YOLO confidence threshold (lower = more sensitive).
"""
def __init__(
self,
detector_path: str | Path = DETECTOR_PATH,
recognizer_path: str | Path = RECOGNIZER_PATH,
det_conf: float = 0.30,
):
self.detector, = load_detector(detector_path),
self.det_conf = det_conf
self.model, self.ckpt = load_recognizer(recognizer_path)
def __call__(self, image: str | Path | np.ndarray) -> dict:
"""
Run full pipeline on a passport image.
Args:
image: file path (str/Path) or BGR numpy array.
Returns dict with keys:
series — 2 Cyrillic letters (e.g. "НР")
number — 6 digits (e.g. "430098")
full — series + number (e.g. "НР430098")
confidence — avg softmax prob (0.0 – 1.0)
readable — False if confidence < threshold
roi_box — (x1,y1,x2,y2) or None if detection failed
"""
# -- Load image --------------------------------------------------------
if isinstance(image, (str, Path)):
img_bgr = cv2.imread(str(image))
if img_bgr is None:
raise FileNotFoundError(f"Cannot read image: {image}")
else:
img_bgr = image
# -- Detect ROI --------------------------------------------------------
box = detect_roi(img_bgr, self.detector, conf=self.det_conf)
if box is None:
return {
"series": None,
"number": None,
"full": None,
"confidence": 0.0,
"readable": False,
"roi_box": None,
"error": "ROI not detected",
}
roi_crop = crop_roi(img_bgr, box, padding=0.0)
# -- Preprocess --------------------------------------------------------
roi_clean = preprocess(roi_crop)
# -- Recognize ---------------------------------------------------------
text, conf = recognize(roi_clean, self.model, self.ckpt)
return {
"series": text[:2],
"number": text[2:],
"full": text,
"confidence": round(conf, 4),
"readable": conf >= CONFIDENCE_THRESHOLD,
"roi_box": box,
}
# -- CLI -----------------------------------------------------------------------
def _show_result(img_bgr: np.ndarray, result: dict) -> None:
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
box = result.get("roi_box")
annotated = img_bgr.copy()
if box:
x1, y1, x2, y2 = box
cv2.rectangle(annotated, (x1, y1), (x2, y2), (0, 255, 0), 3)
fig, axes = plt.subplots(1, 2, figsize=(14, 5))
axes[0].imshow(cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB))
axes[0].set_title("Detected ROI", fontsize=10)
axes[0].axis("off")
if box:
roi = crop_roi(img_bgr, box, padding=0.0)
roi_clean = preprocess(roi)
axes[1].imshow(roi_clean, cmap="gray")
label = result.get("full") or "NOT DETECTED"
conf = result.get("confidence", 0)
axes[1].set_title(f"{label} (conf {conf:.1%})", fontsize=12)
axes[1].axis("off")
plt.tight_layout()
plt.show()
if __name__ == "__main__":
ap = argparse.ArgumentParser(description="Ukrainian passport OCR")
ap.add_argument("image", help="Path to passport image")
ap.add_argument("--show", action="store_true", help="Show visual result")
ap.add_argument("--det-conf", type=float, default=0.30,
help="YOLO detection confidence threshold")
args = ap.parse_args()
ocr = PassportOCR(det_conf=args.det_conf)
result = ocr(args.image)
if result.get("error"):
print(f"ERROR: {result['error']}")
else:
status = "OK" if result["readable"] else "LOW CONFIDENCE"
print(f"Result: {result['full']}")
print(f"Series: {result['series']}")
print(f"Number: {result['number']}")
print(f"Confidence: {result['confidence']:.1%} [{status}]")
if args.show:
img = cv2.imread(args.image)
_show_result(img, result)
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