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ESP32-CAM AI Currency Classifier: Build Real-World Recognition

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2026-02-04 | By Rinme Tom

License: General Public License ESP32

ESP32-CAM AI Currency Recognition: A Complete Edge AI Project for Makers

Imagine transforming an affordable microcontroller camera module into a machine-learning-powered system that can identify and classify Indian currency notes in real time — without internet or cloud services. In this project, you’ll learn how to combine the popular ESP32-CAM Currency Recognition with the Edge Impulse TinyML platform to build a robust currency recognition solution that runs completely at the edge — right on the device itself.

This guide takes you step-by-step — from hardware setup and model training to deployment and real-world testing — making advanced AI accessible to hobbyists, students, and professionals alike.

ESP32-CAM Currency Recognition

Why This Project Matters

Traditional AI systems often rely on cloud servers to process data, introducing latency, connectivity requirements, and privacy concerns. In contrast, edge AI — running machine learning directly on devices like microcontrollers — enables:

  • Instant predictions with minimal delay

  • Offline operation without internet dependency

  • Lower power consumption and data privacy

In this project, you’ll apply these concepts using an ESP32-CAM and a custom TinyML model trained with Edge Impulse to recognize different denominations of Indian rupee notes in real time.

What You’ll Build

You will create a system that:

  1. Captures images of currency notes using an ESP32-CAM

  2. Processes and labels the images using a TinyML model

  3. Runs the model locally on the ESP32-CAM

  4. Outputs results via LEDs and the Arduino Serial Monitor

This hands-on project introduces embedded AI techniques, image classification, and real-world deployment workflows.

Hardware Setup

Since the ESP32-CAM doesn’t include a built-in USB interface, you’ll need a USB-to-Serial converter to program it. Connect the ESP32-CAM’s TX/RX, 5 V, and GND pins to your converter and interface multiple LEDs — each representing a currency denomination — through current-limiting resistors.

Pro tip: Maintain a consistent camera angle and lighting during setup to improve recognition accuracy.

Hardware Set up

Workflow Overview

1. Data Collection

Use the ESP32-CAM web server to collect image samples of each currency denomination. Connect your ESP32-CAM to a Wi-Fi network in programming mode to capture and download training images. Aim for at least 50 images per denomination — more data generally improves model performance.

2. Model Training on Edge Impulse

  • Create a project on Edge Impulse

  • Upload and label your dataset

  • Configure an impulse with Image Processing and Object Detection

  • Train and evaluate your model

The platform will generate an Arduino library with your trained model ready for deployment.

3. Deployment and Testing

Add the generated library to your Arduino environment, adjust the camera definition to match your ESP32-CAM board, and upload the classifier. Once running, place currency under the camera — and watch LED indicators and the Serial Monitor report predictions in real time.

Circuit Diagram

Real-World Performance and Use Cases

When deployed, the edge AI system can recognize different rupee notes, illuminating the corresponding LED to confirm the detected value. This reliable, offline AI system has exciting applications, such as:

  • Assistive tools for visually impaired users

  • Retail automation and point-of-sale systems

  • Automated note counting and validation

  • Smart vending machines and kiosks

Edge Impluse

Future Enhancements

Once your basic version is running, consider expanding your project by:

  • Adding support for more denominations or coins

  • Integrating real-time audio feedback

  • Enhancing the user interface with LCD/OLED displays

  • Incorporating fake note detection through expanded training data

Conclusion

This project exemplifies how affordable hardware and accessible machine learning tools can empower makers to build practical, intelligent systems. By deploying a TinyML model on an ESP32-CAM, you gain real-world experience in edge computing, embedded AI, and embedded system integration.

Whether you’re a hobbyist eager to experiment with edge AI or an educator looking to teach practical TinyML concepts, this currency recognition system is a rewarding and highly adaptable project.

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