Ukrainian Passport Series/Number OCR

Automatic recognition of the series and number from Ukrainian internal passports (старого зразка).

The system reads the dot-matrix perforated 8-character string — 2 Cyrillic letters + 6 digits — from a full passport photo.

НР 430098
^^─────── series  (one of: А В Е К М Н О Р С Т И Ю)
   ^^^^^^ number  (6 digits)

Pipeline

passport image
      │
      ▼
 YOLO detector          ← finds the dotted strip (class: "Dotted")
      │
      ▼
  preprocessing         ← grayscale → denoise → Otsu binarisation
      │
      ▼
  CRNN recognizer       ← CNN + biGRU + 8 position heads → "НР430098"
      │
      ▼
{"series": "НР", "number": "430098", "confidence": 0.97}

Quick start

pip install -r requirements.txt

python pipeline.py passport.jpg
python pipeline.py passport.jpg --show

Output:

Result:     НР430098
Series:     НР
Number:     430098
Confidence: 97.3%  [OK]

Python API

from pipeline import PassportOCR

ocr    = PassportOCR()
result = ocr("passport.jpg")

print(result["full"])        # "НР430098"
print(result["series"])      # "НР"
print(result["number"])      # "430098"
print(result["confidence"])  # 0.9734
print(result["readable"])    # True

Models

File	Architecture	Size	Purpose
`models/detector.pt`	YOLO nano fine-tuned	5.95 MB	Locates the dotted strip on the passport page
`models/recognizer.pth`	CNN + biGRU (FP32)	2.25 MB	Reads the 8-character string from the strip
`models/recognizer_fp16.pth`	CNN + biGRU (FP16)	1.13 MB	Same accuracy, 2× smaller — recommended for deployment

To use the FP16 model:

ocr = PassportOCR(recognizer_path="models/recognizer_fp16.pth")

The recognizer is trained on 80 000 synthetic images generated with a custom dot-matrix Cyrillic font, with heavy augmentation (rotation ±20°, perspective, noise, donut dots, shadow stripes, inversion).

Val character accuracy: 96.1% | Full-string accuracy on real passports: 96%

Training

# Generate synthetic training data
cd train
python generate_sequences.py --n-samples 80000

# Train the recognizer
python train_sequence.py --epochs 35 --batch-size 64

To retrain the YOLO detector, annotate passport images with the "Dotted" class and run standard YOLOv8 training.

Series letters

Valid Ukrainian passport series letters: А В Е К М Н О Р С Т И Ю

The model enforces positional constraints: positions 0–1 accept only series letters, positions 2–7 accept only digits.

Custom fonts & datasets

The train/font_editor.py tool lets you define your own dot-matrix font for any character set:

Add / remove any Unicode character
Set any grid size per character (cols × rows, independently)
Save to font_custom.json, export to font_custom.py

cd train
python font_editor.py                      # edit default font
python font_editor.py --font myfont.json   # open a different font file

After editing, regenerate synthetic data and retrain:

python generate_sequences.py --n-samples 80000
python train_sequence.py --epochs 35

Limitations

Works on Ukrainian internal passports (старого зразка, до 2016 року)
Requires the dot-matrix strip to be visible and not heavily physically damaged
Very low contrast or extreme blur may result in readable: False
Low-confidence predictions (< 50%) are flagged — the raw image may be unreadable

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