Edge-AI Currency Classifier with ESP32-CAM & Edge Impulse — Low-Cost Smart Recognition System

fritzing

Grove - OLED Display 0.96"

Arduino IDE

40 colored female-female jumper wires

ESP32 CAM

Breadboard 100x50

<html><body><h3>Overview</h3>In this <a href="https://circuitdigest.com/microcontroller-projects/esp32-cam-currency-recognition-using-edge-impulse">ESP32 CAM Currency Recognition </a>, you will turn an affordable ESP32-CAM board into an intelligent currency note classifier using the power of edge machine learning (TinyML) — all without needing continuous internet access or costly cloud resources. By leveraging the Edge Impulse platform for dataset collection, model training, and edge deployment, your device can accurately identify Indian rupee denominations in real time. This tutorial is perfect for Arduino enthusiasts, makers, and students looking to explore embedded AI applications, real-world computer vision, and edge-based inference on microcontrollers. From basic circuits to complex designs, check out the <a href="https://circuitdigest.com/esp32-projects">ESP32 Projects </a>. <h3>What You’ll Achieve</h3>By the end of this project, your ESP32-CAM will be able to: ✔ Capture images of Indian currency notes ✔ Process and classify them using a TinyML model trained via Edge Impulse ✔ Provide visual output with LEDs for each denomination ✔ Display recognition results over the Arduino Serial Monitor ✔ Run autonomously without Wi-Fi after deployment <figure><img src="https://projects.arduinocontent.cc/6e067a01-8f0b-41a2-8c02-dd5343f27908.jpg"/><figcaption>Hardware Set up</figcaption></figure> <h3>Core Learning Outcomes:</h3>🔹 TinyML fundamentals and edge AI concepts 🔹 ESP32-CAM hardware interfacing 🔹 Image data collection and labeling workflows 🔹 Edge Impulse model design and deployment 🔹 Arduino programming and real-time inference <h3>Why This Project Matters</h3>Traditional AI applications rely on remote cloud services, which can introduce latency, privacy concerns, and require continuous internet access. With edge AI, the intelligence runs directly on the device, offering faster response, reduced data transfer costs, and enhanced privacy — ideal for low-resource embedded systems like the ESP32-CAM. <figure><img src="https://projects.arduinocontent.cc/6a2b947f-c193-4f81-a438-4abdd49fb6e4.jpg"/><figcaption>Connectiions</figcaption></figure> <h3>Circuit Setup Summary</h3>Since the ESP32-CAM lacks onboard USB, you’ll use a USB-to-Serial converter to upload code and monitor output. Connect: FTDI TX → ESP32-CAM RX FTDI RX → ESP32-CAM TX FTDI 5V &amp; GND → ESP32-CAM 5V &amp; GND Each LED is connected via a 100Ω resistor to different GPIOs Common ground for all LEDs Once a note is identified, its corresponding LED lights up, providing intuitive feedback. <figure><img src="https://projects.arduinocontent.cc/5ea3e977-2293-4f12-893f-deca0d80612e.png"/><figcaption>Circuit Diagram</figcaption></figure> <h3>Step-by-Step Workflow</h3>1️⃣ Data Collection &amp; Preparation Use the ESP32-CAM web interface to collect images of each currency denomination. Ensure consistent lighting and simple backgrounds for higher model accuracy. Capture multiple images (~50+ per class) and download them for labeling. 2️⃣ Edge Impulse Model Creation Create a new project on Edge Impulse Upload your captured images Label each sample with the correct denomination Configure impulse: 96×96 image size, grayscale processing, and object detection learning Train and evaluate the model until acceptable accuracy is achieved 3️⃣ Deployment to Arduino Export the Edge Impulse model as an Arduino library Import the generated files into Arduino IDE Adjust the code for the AI-Thinker ESP32-CAM board Upload and verify stability 4️⃣ Testing &amp; Calibration Mount the ESP32-CAM on a fixed holder to keep the camera angle consistent. Present each note and observe LED responses and Serial Monitor classification outputs to verify successful recognition. <figure><img src="https://projects.arduinocontent.cc/5c2642bf-8b5e-4016-be24-a2a59a4ee623.jpg"/><figcaption>Working Demo</figcaption></figure> <h3>Practical Uses</h3>This project has real-world potential across multiple domains: ✨ Assistive tool for visually impaired users (with audio or display modules added) ✨ Retail cash handling and automated POS validation ✨ Vending machines with smart currency validation ✨ Automated cash counters in small businesses <h3>Future Enhancements</h3>You can expand this project further by: 🔹 Supporting more denominations (including coins) 🔹 Adding voice announcements using a speaker module 🔹 Integrating an OLED/LCD to show results visually 🔹 Incorporating fake currency detection via extended training data <h3>Conclusion</h3>This project demonstrates how a tiny device like the ESP32-CAM can be transformed into a sophisticated TinyML-powered currency recognition system. It blends hardware interfacing, machine learning, and real-time embedded programming to bring computer vision to life on a budget. Start building your own smart currency classifier today — and unlock the potential of edge AI in your <a href="https://circuitdigest.com/arduino-projects">Arduino Projects </a><figure><iframe src="https://youtu.be/H6dCoPK2uA8"></iframe></figure> </body></html>

Edge-AI Currency Classifier with ESP32-CAM & Edge Impulse — Low-Cost Smart Recognition System

Build a real-time Indian currency recognition system using ESP32-CAM and TinyML with LEDs and serial output for intuitive feedback.