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Automatic License Plate Recognition Using YOLOV8

A Python video-processing application that uses YOLOv8-based models to detect vehicles and license plates, tracks detections across frames, interpolates missing values and generates a smoothed visualization. The repository documents CSV output and identifies SORT as a tracking dependency; it does not specify OCR text output.

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Independent OpenViST project profile, based on linked sources.

Automatic License Plate Recognition Using YOLOV8 — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to examine or adapt a documented Python workflow that processes video into tracked vehicle and plate detections, interpolated CSV data and a visualization.

Who it is for

  • Developers examining a YOLOv8 vehicle and license-plate detection workflow.
  • Computer-vision practitioners who want to inspect video detection, tracking and interpolation stages.

Documented uses

  • Generate vehicle and license-plate detections from video.
  • Track detections across frames and save frame-level results as CSV.
  • Interpolate values for missing frames and visualize smoothed detections.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. Its setup instructions specify Python 3.10 and Conda.

  • The README names YOLOv8n for vehicle detection and a separately trained YOLOv8 license-plate detector.
  • SORT must be downloaded separately, as directed by the README.
  • The documented processing stages are split across main.py, add_missing_data.py and visualize.py.
  • The README does not describe OCR text results, despite EasyOCR appearing in repository topics.
  • A change to other object classes or models is described as likely compatible, but is not documented or tested.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run the documented sample video workflow

Generate detection results, interpolate missing-frame values and create a smoothed visualization from the sample video.

Before you start

  • Python 3.10 and Conda
  • Project files and dependencies from the repository
  • SORT module from https://github.com/abewley/sort
  • The sample video linked in the README

Steps

  1. Create and activate a Conda environment with Python 3.10, following the README commands.

  2. Install project dependencies with pip install -r requirements.txt.

  3. Run python main.py to generate test.csv from the sample video.

  4. Run python add_missing_data.py, then python visualize.py to interpolate missing values and obtain the visualization.

Expected outcome

A CSV of detection results followed by interpolated data and a smoothed license-plate detection visualization.

Guide limitations

  • The README describes a sample-video workflow; it does not document OCR text output or alternate datasets and models.
  • The guide reflects repository instructions and is not a human execution test.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
Muhammad-Zeerak-Khan/Automatic-License-Plate-Recognition-using-YOLOv8 · Official homepage title · retained in its source language
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