How to Build a Vehicle License Plate Recognition System with Python and OpenCV

1. License Plate Detection and Recognition Project Overview

License plate detection and recognition is widely used in traffic‑violation tracking, community or underground‑garage access control. Because plates are regular rectangles with relatively fixed aspect ratio, color and texture, traditional methods rely on shape, color, texture, and character features, while recent deep‑learning approaches use object‑detection models.

The workflow is:

Input raw image, apply binarization, edge detection, and color‑tuning to locate the plate region.

Crop the detected plate (ROI) for further processing.

Use histogram peak‑valley segmentation to split characters.

Train two SVM models: one for province abbreviation, another for alphanumeric characters.

Wrap the whole algorithm in a PyQt5 GUI and package it as an installable application.

2. Project Code Structure

The repository https://github.com/DataXujing/vehicle-license-plate-recognition contains the full source code; the diagram below shows its layout.

3. Project Demonstration

The application can be tested by downloading the installer from the Baidu link (extraction code: v103) or by running the source directly. Screenshots of recognition results and test videos are shown below.

4. TODO

The current recognition performance is still unsatisfactory in some scenarios. Two main technical reasons are:

The detection algorithm sometimes fails to locate the plate; more robust object‑detection models such as Faster R‑CNN, YOLO, or SSD could be tried.

The recognition model is based on a small training set and simple SVMs; expanding the dataset and using more powerful models like XGBoost, LightGBM, CatBoost, or a CNN (or attention‑based CNN‑RNN OCR) may improve accuracy.

Reference

https://blog.csdn.net/sumkee911/article/details/79435983

License‑Plate‑Recognition python + OpenCV

License-Plate-Recognition

车牌识别（一）-车牌定位

在PyQt5中美化和装扮图形界面