Midv-679

Overview MIDV-679 is a widely used dataset for document recognition tasks (ID cards, passports, driver’s licenses, etc.). This tutorial walks you from understanding the dataset through practical experiments: preprocessing, synthetic augmentation, layout analysis, OCR, and evaluation. It’s designed for researchers and engineers who want to build robust document understanding pipelines. Assumptions: you’re comfortable with Python, PyTorch or TensorFlow, and basic computer vision; you have a GPU available for training.

What you’ll learn

Note: this tutorial is implementation-focused and includes runnable code sketches and recommended libraries so you can reproduce experiments quickly.

| Mode | Description | Typical Use | |------|-------------|-------------| | Live View | Streams sensor data (temp, humidity, accel) in real time. | Quick field checks. | | Log Capture | Records data to internal storage (default: /var/log/midv). | Long‑term monitoring (up to 48 h with default 4 GB). | | External I/O | Accepts analog (0‑5 V) via optional ADC module (M.2 slot). | Custom sensor integration. | | USB‑C Device | Acts as a host for USB sticks or measurement instruments. | Data import/export, firmware flashing of peripherals. | MIDV-679

How to start a Log Capture:

File layout (typical):

Quick loader sketch:

import json, cv2, os
from glob import glob
image_paths = glob("MIDV-679/images/*.jpg")
ann_paths = os.path.basename(p).split('.')[0]: p for p in glob("MIDV-679/annotations/*.json")
def load_example(img_path):
    key = os.path.basename(img_path).split('.')[0]
    ann = json.load(open(ann_paths[key]))
    img = cv2.imread(img_path)[:,:,::-1]  # RGB
    quad = ann['quad']  # e.g., list of 4 (x,y)
    return img, quad

Visual inspection: display many examples of the variety—lighting, blur, background clutter. This helps pick augmentations.