Aeonix Research & Innovations

🚗 ANPR IN ACTION — LIVE LAB DEMO!

We’ve talked about ANPR, OCR, Edge AI, and hardware choices throughout this series. Now it’s time to see what happens when everything comes together. 🔥

In this lab demo, our ANPR system is actually capturing a vehicle, detecting its number plate, processing the image locally, and returning the recognition result in real time.

The complete flow:

📷 Capture → 🧠 Process Locally → 🔎 Recognize → ✅ Get Results

This is the real goal of an engineering project — not just building a model, but turning it into a reliable, working system.

And this is exactly where Edge AI becomes interesting: lower latency, local processing, and the ability to operate without depending entirely on cloud connectivity.

🎥 Watch the full demo and see our ANPR system working in the lab.

What would you like us to demonstrate next — OCR accuracy, real-time performance, or the complete ANPR pipeline? 👇

Raspberry Pi vs Jetson Orin Nano — which one would you choose for ANPR? 🚗💻

Raspberry Pi can handle a lot… but ANPR is not just about running software. It’s about real-time computer vision, AI inference, accuracy and performance.

That’s why, for our local ANPR system, we're going with Jetson Orin Nano.

In this reel, we explain:
🔴 Where Raspberry Pi starts to struggle
🟢 Why Jetson Orin Nano fits our workload better
⚡ What real-time edge AI actually demands
🎯 Why hardware choice directly affects ANPR performance

If you were building an ANPR system, what would you pick — Raspberry Pi or Jetson?

Drop your choice in the comments 👇

How does a machine actually read a license plate? 🤔🚗

That's where OCR (Optical Character Recognition) comes in.

For our ANPR project, we explored OCR solutions like EasyOCR and PaddleOCR and looked at how they handle the challenges of real-world license plate recognition.

The goal isn't just to detect a vehicle — it's to reliably convert the plate image into usable text, quickly and accurately. ⚡

But here's the interesting part:

👉 EasyOCR vs PaddleOCR — which one would you pick for an ANPR system?

Watch the reel and let us know your choice in the comments. 👇

Building a Local ANPR System with Edge AI 🚗🤖

What if a number plate recognition system didn’t need to send every frame to the cloud?

That’s exactly what we’re exploring.

We’re building an Automatic Number Plate Recognition (ANPR) system that performs plate detection + OCR + recognition directly on an edge device.

The goal is simple:

⚡ Real-time processing
🔒 Better privacy
📡 Less dependency on connectivity
🧠 AI running locally

From capturing the image to recognizing the number plate — everything happens at the edge.

This is the kind of project where AI meets real hardware. And we’re just getting started. 🚀

Building a Local ANPR System 🚗📷

We’re working on an Automatic Number Plate Recognition (ANPR) system that can detect and recognize vehicle number plates locally — without relying entirely on cloud processing.

The goal is simple: capture locally, process locally, recognize in real time.

🔹 Real-time plate detection
🔹 Local/edge processing
🔹 Reduced cloud dependency
🔹 Better control over data
🔹 Built for practical deployment

This is more than just AI running on a camera — it’s about turning Edge AI into a reliable, deployable system.

What would you use a local ANPR system for — parking, access control, security, or something else?

If a vendor can't answer these three, they're reselling someone else's SDK.

🌍 How do we make our products work in any country?

The answer starts with designing for the world. 📡

This global LTE modem, Quectel EG800AK-GL, allows us to build one product that can be deployed across different countries and connect to supported local cellular networks wherever it goes. 🌐

No country-specific redesigns.
No starting from scratch.

Design once. Build smart. Deploy globally. 🚀

That’s how we think when building connected products for a global market. 🌍⚡

⚠️ **Your ESP32 isn't always crashing because of bad code.**

Sometimes, the real culprit is hiding somewhere much simpler: **your power supply.** ⚡

During operations such as Wi-Fi transmission, the ESP32 can draw sudden bursts of current. If the power source cannot supply that demand, the voltage may dip.

The result?

🔻 Voltage drop
⚠️ Brownout detection
🔄 ESP32 reset

And suddenly, it looks like your firmware has crashed.

Before spending hours rewriting and debugging your code, take a look at your power supply, wiring and power stability.

**Stable power = stable ESP32 = fewer mysterious resets.** 🚀

Have you ever faced this problem while working with ESP32? Tell us in the comments! 👇