digital-inspector

YOLOv8 Document Inspector

Automatic detection of signatures, stamps, and QR codes in business documents (Russian/Kazakh).

Results (Run 3 — Conservative Augmentation)

Speed vs Accuracy

Performance Comparison

Training Curves

Precision vs Recall

Detection Examples

Key Findings

y8n beats y8l: 3.2M params model outperforms 43.7M params model in both speed (237ms vs 316ms) and accuracy (0.669 vs 0.642).

Usage

from ultralytics import YOLO

model = YOLO("models/y8n_1024/best.pt")
results = model.predict("document.jpg", imgsz=1024, conf=0.25)
results[0].show()

Training Config (Run 3)

model.train(
    imgsz=1024, epochs=150, patience=30,
    degrees=3.0, translate=0.05, scale=0.2,
    hsv_v=0.2, hsv_s=0.1,
    mosaic=0.0,   # OFF — harmful for documents
    fliplr=0.0,   # OFF — text would mirror
    flipud=0.0,
)

Dataset

Provided during a hackathon competition. Contains annotated Russian/Kazakh business documents. Dataset is not publicly available due to privacy constraints.

Source code: github.com/AlihanSDev/digital-inspector