ANPR Server C

A multithreaded Automatic Number Plate Recognition (ANPR) server written in C++, much like OpenALPR. Difference is that this code is build for speed & low-overhead, which makes it run on embedded devices like a raspberry PI. Also uses deep neural-networks (DNNs, trained with Caffe) for character classification through the new OpenCV DNN interface.

AI & Machine LearningC++GPL-3.0

Abstract

ANPR Server C is an open-source AI & Machine Learning project. A multithreaded Automatic Number Plate Recognition (ANPR) server written in C++, much like OpenALPR. Difference is that this code is build for speed & low-overhead, which makes it run on embedded devices like a raspberry PI. Also uses deep neural-networks (DNNs, trained with Caffe) for character classification through the new OpenCV DNN interface. It is built using C++. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

A multithreaded Automatic Number Plate Recognition (ANPR) server written in C++, much like OpenALPR.

2. Objective

A multithreaded Automatic Number Plate Recognition (ANPR) server written in C++, much like OpenALPR. Difference is that this code is build for speed & low-overhead, which makes it run on embedded devices like a raspberry PI. Also uses deep neural-networks (DNNs, trained with Caffe) for character classification through the new OpenCV DNN interface.

This project demonstrates how C++ can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

C++

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Arduino IDE / PlatformIO or a C++ compiler (g++)
  • Target board (e.g. Arduino, ESP32) where applicable
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/srslynow/ANPR-Server-C-.git
cd ANPR-Server-C-
  1. C++17 capable compiler
  2. OpenCV >3.3.x (or earlier version, with a manually linked opencv-DNN module, from opencv-contrib)
  3. SFML >2.x
  4. socket_send_udp.py
  5. socket_send_udp_video.py
  6. Initial packet is 4 bytes (int) indicating frame data size, endian format is dependend on ANPR-Server host. Most systems use Little-endian, as does mine.
  7. Following packets contain JPG-encoded frame data, keep packet size <1500 bytes to prevent fragmentation
  8. Repeat for more frames.. :)

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by srslynow and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship