Globally, approximately 2.2 billion people suffer from visual impairments, with 90% residing in low-income countries. For these individuals, affordable and accessible solutions are crucial. In this project, I demonstrate how computer vision can significantly improve accessibility by enabling visually impaired individuals to read independently.
The challenge faced by visually impaired individuals is their limited ability to perceive and navigate their surroundings, which can restrict their mobility. To address this, I developed a system that combines object detection and text-to-speech conversion to provide real-time reading assistance. The result is a device that helps visually impaired users understand their environment through audio feedback.
Building the Solution with Seeed Studio XIAO ESP32S3The heart of this project is the Seeed Studio XIAO ESP32S3 microcontroller, chosen for its compact size and powerful processing capabilities. This device is integrated with several key components to bring the project to life.
The OV2640 camera sensor is used to capture high-resolution images of printed text. This visual data is then processed to extract the text content.
- Capturing Text with the OV2640 Camera SensorThe OV2640 camera sensor is used to capture high-resolution images of printed text. This visual data is then processed to extract the text content.
- Text Recognition with Tesseract.jsTesseract.js is employed to perform Optical Character Recognition (OCR) on the captured images. This software converts visual text into readable digital text and supports more than 100 languages.
- Text-to-Speech Conversion with Google TTSThe recognized text is converted into natural-sounding speech using Google Text-to-Speech (TTS), providing clear audio output for the user. Google's Text-to-Speech (TTS) API supports over 50 languages and variants, with a selection of more than 380 voices.
- Audio Enhancement with MAX98357A Amplifier and DIY SpeakerThe MAX98357A amplifier boosts the audio signal, which is then played through a DIY speaker, ensuring the speech is loud and clear.
The prototype is built using the following hardware components: the Seeed Studio XIAO ESP32S3 microcontroller, a MAX98357A I2S digital-to-analog converter (DAC) and amplifier, and a speaker. Let's go through the schematic step-by-step to understand how these components are connected:
Here is how the components are connected according to the schematic:
- Power Connections:5V (VCC) from the Seeed Studio XIAO ESP32S3 is connected to the VCC pin of the MAX98357A. GND (Ground) from the Seeed Studio XIAO ESP32S3 is connected to the GND pin of the MAX98357A to establish a common ground between both components.
- I2S Data Connections:The BCLK (Bit Clock) pin on the MAX98357A is connected to the SCK (Serial Clock) pin on the XIAO ESP32S3. This pin transmits the clock signal for the digital audio data. The LRC (Left-Right Clock) pin on the MAX98357A is connected to the LRCK (Word Select) pin on the XIAO ESP32S3. This pin indicates whether the audio data corresponds to the left or right channel. The DIN (Data In) pin on the MAX98357A is connected to the SD (Serial Data) pin on the XIAO ESP32S3. This pin is used to transmit the digital audio data from the microcontroller to the DAC.
- Speaker Connections:The + and - terminals of the speaker are connected to the + and - output pins on the MAX98357A amplifier, respectively. This connection allows the amplified analog audio signal to be output to the speaker.
- Programming and IntegrationThe project begins with programming the microcontroller using Embedded C and integrating it with Node.js for managing data flow. Node.js sends captured images to Tesseract.js and receives the extracted text, which is then sent to the Arduino IDE.
- Creating and Deploying the ModelThe captured text is processed using Tesseract.js to ensure accurate recognition. The model is then deployed onto the XIAO ESP32S3 microcontroller. For deployment, the Arduino IDE is used to upload the firmware that integrates all components.
- Testing and CalibrationThe device is tested to ensure accurate text recognition and clear audio output. Adjustments are made to improve performance and accuracy based on testing results.
To get the device up and running, follow the software setup instructions below. This setup involves configuring the Arduino IDE, integrating necessary libraries, setting up the OCR processing with Tesseract.js, and installing Google Text-to-Speech for audio output.
1. Installing Arduino IDE and Required Libraries
- Download and Install Arduino IDE:Visit the Arduino website and download the latest version of the Arduino IDE suitable for your operating system. Install the software following the instructions provided on the website.
- Install the ESP32 Board Package:Open the Arduino IDE, go to File > Preferences, and add the following URL to the Additional Board Manager URLs field:
https://dl.espressif.com/dl/package_esp32_index.json
- Next, navigate to
Tools > Board > Boards Manager
, search for "ESP32, " and install the package. - Add Required Libraries:Go to
Sketch > Include Library > Manage Libraries...
, and search for and install ArduinoJson library
2. Setting Up Node.js for OCR
Tesseract.js is a powerful JavaScript OCR engine that will run on the Node.js environment. To set it up:
- Install Node.js and npm (Node Package Manager):If you haven't already, download and install Node.js from the official website. This installation will include npm (Node Package Manager), which is needed to install dependencies.
- Initialize a Node.js Project:Open a terminal or command prompt, navigate to your project directory, and run:
mkdir ocr-server
cd ocr-server
npm init -y
- Install Required Node.js Modules: Install
express
for creating the server andtesseract.js
for OCR processing:
npm install express tesseract.js
3. Writing the Code
- Open the Arduino IDE and create a new sketch. In the new sketch, add the code.
- Create a new file named
server.js
in the ocr-server directory and add the server code for ocr processing. - To start the OCR server, run the following command in your terminal or command prompt:
node server.js
- You should see a message indicating that the server is running
OCR server is running at http://localhost:3000
- Connect your microcontroller to the computer via a USB cable, select the appropriate board and port from the
Tools
menu, and click the upload button. - Open the Serial Monitor in the Arduino IDE (
Tools > Serial Monitor
) to check for errors or debug messages.
The device not only converts printed text into speech but also provides real-time feedback to help users position the device correctly for optimal performance. If the text is not readable due to improper positioning or distance, the device will give an audio prompt such as:
"Please keep the device at least 15 cm above the text."
This ensures that the camera can focus properly and capture a clear image of the text. The voice command helps users adjust the device to the correct height and angle, minimizing errors in text recognition and improving overall user experience.
Final IntegrationThe completed system combines the XIAO ESP32S3 microcontroller, camera sensor, text recognition software, and audio components into a cohesive device. Users can now point the camera at printed text, which is instantly converted to speech, allowing them to read the content. Additionally, with the built-in guidance system, users receive instant feedback to ensure the device is positioned correctly for accurate text capture.
This project represents a significant step forward in creating accessible technology for the visually impaired. By providing an affordable, real-time reading aid with user guidance, it offers a powerful tool for enhancing independence and improving quality of life.
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