Handling cash is something most of us do without thinking. But for visually impaired people, identifying currency notes can be a daily challenge.
Many people learn to recognize notes by touch, but that becomes harder with age. An elderly shopkeeper or street vendor with weak eyesight may struggle to verify the denomination handed over by customers. A simple mistake can lead to financial loss.
To address this problem, I built an ESP32-CAM Indian Currency Recognition. The idea is simple: point a camera at a currency note, press a button, and hear the denomination announced through a speaker.
The Idea Behind the Project
The system uses an ESP32-CAM to capture an image of an Indian currency note.
Instead of running a machine learning model directly on the microcontroller, the captured image is sent to a cloud-based AI service. The cloud processes the image, identifies the denomination, and sends the result back to the ESP32-CAM. The system then converts the result into speech and announces it through a speaker.
The entire process takes only a few seconds.
Why Use ESP32-CAM?
The ESP32-CAM is one of the most affordable boards available for computer vision projects.
It combines Wi-Fi connectivity, a camera interface, onboard processing, and compact size into a single board. This makes it perfect for projects that need image capture without requiring expensive hardware.
For student projects, it is often the easiest way to get started with AI-powered vision applications.
Hardware Required
The hardware setup is surprisingly simple.
You only need:
- ESP32-CAM
- PAM8403 audio amplifier
- Speaker
- Push button
The push button triggers image capture, the ESP32-CAM handles image transmission, and the speaker announces the detected denomination.
How the System Works
The workflow is straightforward.
First, the user places a currency note in front of the camera and presses the button. The ESP32-CAM captures a high-quality image and uploads it to the cloud recognition API. The cloud analyzes the note and returns the detected denomination.
Once the result arrives, the system generates audio using text-to-speech and plays it through the speaker. Instead of reading a display, the user simply listens to the spoken output.
This makes the system far more accessible for visually impaired users.
Why CircuitDigest Cloud Instead of Running ML on the ESP32?
This was one of the most interesting parts of the project.
Traditional machine learning workflows usually require collecting hundreds of images, labeling datasets, training models, optimizing them, and then deploying them to embedded hardware.
That process can take days.
In this project, the heavy lifting happens in the cloud. The ESP32-CAM only captures and uploads images. The recognition model is already trained and maintained on the server side, making development much faster.
For beginners, this removes one of the biggest barriers to building AI-powered projects.
Audio Feedback Makes a Huge Difference
The recognition itself is useful, but the audio output is what makes the system practical.
After identifying the note, the ESP32-CAM uses a text-to-speech service to generate spoken audio. The amplifier boosts the sound, and the speaker announces the denomination clearly.
This means users don't need a display or smartphone screen to understand the result.
Challenges I Faced
Like most vision-based projects, image quality matters.
Poor lighting, folded notes, camera shake, or blurry images can affect recognition accuracy. During testing, keeping the note around 10–15 cm from the camera and using proper lighting produced the most reliable results.
A stable Wi-Fi connection is also important since the image must be uploaded before processing can begin.
Where This Can Be Used
While this project was designed for visually impaired users, there are several other use cases.
It can be used in small shops, educational demonstrations, assistive devices, banking kiosks, or even as part of a larger financial accessibility system.
The same concept can also be expanded to recognize foreign currencies, documents, or everyday objects.
What I like most about this project is that it combines embedded systems, cloud AI, computer vision, and accessibility into a single build.
The hardware is simple, the setup is beginner-friendly, and the result solves a real-world problem. More importantly, it shows how affordable hardware like the ESP32-CAM can be used to create practical assistive technology that genuinely helps people become more independent.
Top comments (0)