Headgear that could help people with visual impairment perceive text by reading it out aloud in real-time isn’t a novel concept but making it by yourself at home for your loved ones might be – and an eighth-grade student has succeeded in doing just that.
Indeed, Akhil Nagori, who is a member of the 49ers STEM Leadership Institute, has created ‘glasses’ that aren’t intended for traditional seeing but are outfitted with hardware to capture text and read it aloud, sharing the entire process online in both text and video on YouTube, helping others accomplish the same.
Specifically, the setup is pretty simple for anyone with just a tad of technological inclination and includes a Raspberry Pi Zero 2W that runs off a 3.7-volt lithium-ion battery (ideally with more than 1.2 amps) and is outfitted with a classic first-party camera, like the Mini Raspberry Pi Camera Module.
The camera is mounted on a set of eyeglass frames so that it points at whatever the person wearing it might be ‘looking’ at. All it takes is a push of a button, and the camera will capture the image, passing it on to an API that does the optical character recognition, and further onto a speech synthesizer so it can be read aloud to the wearer.
Other necessary tools include jumper wires (male-to-male), two mini speakers, PLA basic filament (like Bambu Lab’s), MT3608 step-up power boost converter, soldering iron, PCB circuit board, 3D printer, and Raspbian CLI software, with the total cost of such a setup estimated at below $69.
Step 1: Text to audio glasses software
Nagori explained how he deployed the process, which included using a fast API that runs a subprocess that allows it to call the camera capture from inside the code which needs to be uploaded to the Raspberry Pi.
Update your system
To begin, you’ll need to enable SSH on the Raspberry Pi, “so you can use the terminal directly through your other computer.” Furthermore, before installing dependencies, you’ll need to update your Raspberry Pi.
| bash sudo apt update && sudo apt upgrade -y |
Enable the camera
To enable the camera if using Raspberry Pi OS, open Raspberry Pi configuration:
| bash sudo raspi-config |
Go to Interfacing Options → Camera → Enable.
Reboot your Raspberry Pi:
| bash sudo reboot |
Install required software
Then, install the required software, including Python dependencies:
| bash pip install RPi.GPIO requests espeakng opencv-python |
Test if RPi.GPIO is installed:
| bash python3 -c “import RPi.GPIO; print(‘RPi.GPIO is installed!’)” |
Write the Python code
Now it’s time to write the Python code, first creating a new Python script:
| bash nano button_ocr.py |
Paste the following complete script:
| import RPi.GPIO as GPIO import requests import espeakng import cv2 import subprocess import time # Define GPIO pin for the button BUTTON_PIN = 17 # Set up GPIO GPIO.setmode(GPIO.BCM) # Use BCM pin numbering GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # Internal pull-down def capture_and_process(): “””Captures an image, processes it with OCR, and converts text to speech.””” image_path = “captured.jpg” # Capture image using libcamera-jpeg subprocess.run([“libcamera-jpeg”, “-o”, image_path, “–width”, “640”, “–height”, “480”]) # Verify if image was saved image = cv2.imread(image_path) if image is None: print(“❌ Failed to capture image!”) return print(“✅ Image captured successfully!”) # OCR function def ocr_space_file(filename, api_key=’helloworld’, language=’auto’, ocr_engine=2): api_url = ‘https://api.ocr.space/parse/image’ payload = { ‘isOverlayRequired’: False, ‘apikey’: api_key, ‘language’: language, ‘OCREngine’: ocr_engine, } with open(filename, ‘rb’) as f: response = requests.post(api_url, files={‘filename’: f}, data=payload) if response.status_code == 200: result = response.json() if ‘ParsedResults’ in result and result[‘ParsedResults’]: return result[‘ParsedResults’][0].get(‘ParsedText’, ”).strip() else: print(“⚠️ No text found in the image.”) return “” else: print(f”❌ OCR Error: {response.status_code}, {response.text}”) return “” # Run OCR text = ocr_space_file(image_path) if text: print(f”📝 Extracted Text: {text}”) # Convert text to speech tts = espeakng.Speaker() tts.wpm = 100 tts.say(text.replace(“\r\n”, ” “), wait4prev=True) else: print(“⚠️ No text extracted from the image.”) # Main loop to wait for button press print(“🚀 Waiting for button press to capture an image…”) try: while True: if GPIO.input(BUTTON_PIN) == GPIO.HIGH: # Button is pressed print(“🔘 Button Pressed! Capturing image…”) capture_and_process() time.sleep(1) # Debounce delay except KeyboardInterrupt: print(“\n🛑 Program terminated.”) GPIO.cleanup() # Clean up GPIO settings |
Save the file (CTRL + X, then Y, then ENTER).
Running the program
Run the script:
| python3 button_ocr.py The program will wait for a button press. Press the button → It captures an image. The OCR extracts text. The text is spoken using espeakng. |
After taking these steps, you’ll be ready to run the code. Upload the main.py file and tts.py file to the Raspberry Pi, and copy the code from tts.py to the end of main.py, so they run in one execution. Now, you should have working text-to-audio glasses, but you must replace the test image in main.py with <imagename>.jpg.
As Nagori explained, “this will be used later when setting up the sound to the raspberry pi.”
Step 2: Text to audio glasses hardware
Our young programmer then uploaded the files needed to print on a 3D printer, alongside using the software for your printer to slice the uploaded .stl file.
One of the glasses’ images for the 3D printer. Source: Akhil Nagori / Instructables
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Conclusion
Today’s world is more visually-impaired-friendly than before, but there is still plenty of text that isn’t translated in a form understandable to the afflicted (in braille, audio, etc.). With his project, Nagori wanted to address this problem and provide visually impaired people with access to more written text.
As he pointed out:
“We successfully developed a prototype system that uses a Raspberry Pi, a camera module, and a push-button interface to capture images, extract text using Optical Character Recognition (OCR), and read the text aloud using text-to-speech (TTS) technology.”
On top of that, he “implemented OCR using the OCR.space API and integrated espeakng for speech output,” and optimized the system to “function effectively in various lighting conditions and text formats, ensuring accessibility and ease of use.”
Nagori’s project outline. Source: Akhil Nagori / Instructables
This way, Nagori said he had demonstrated the “feasibility of a low-cost assistive device that enhances independence and daily interactions with printed text.” And it’s true – artificial intelligence (AI) glasses might be all the rage now, but solutions such as this one have the chance to help those who can’t use them yet.
Watch the demo of Nagori’s project at work:
