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ProjectsIoT & Embedded SystemsAI-Powered Smart Assistance Spectacles for Blind
AI-Powered Smart Assistance Spectacles for Blind - 1
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IoT & Embedded SystemsFeatured

AI-Powered Smart Assistance Spectacles for Blind

An AI-powered wearable smart spectacle system for visually impaired individuals that combines real-time object detection, OCR text recognition, text-to-speech conversion, and emergency SOS alerts using Raspberry Pi and computer vision.

mca, electronics-communication-engineering, bca9 Tech
PythonRaspberry Pi 4OpenCVTesseract OCRText-to-Speech (TTS)+4
₹13,999₹19,99930% OFF
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DescriptionFeaturesModulesFAQ

About This Project

The AI-Powered Smart Assistance Spectacles for Blind is an intelligent wearable assistive system developed to improve the mobility, safety, and independence of visually impaired individuals. The system utilizes Raspberry Pi as the central processing unit along with a Pi Camera to capture live video. Using the YOLO object detection algorithm, the system detects nearby objects and obstacles in real time while identifying their relative position. EasyOCR or Tesseract OCR extracts text from signboards, labels, and printed documents, and a Text-to-Speech engine converts the extracted text into audible feedback through earphones or speakers. The project also integrates an emergency SOS button that can send alerts with location information during emergency situations. The wearable design makes the solution portable, affordable, and practical for daily use. The system combines Artificial Intelligence, Computer Vision, Optical Character Recognition, and Speech Processing into a single wearable device, providing an effective assistive technology solution for blind and visually impaired users.

Software Details

Category
IoT & Embedded Systems
Department
mca, electronics-communication-engineering, bca
Demo
Available
Rating
4.6 (61 reviews)

Project Timeline

Published

30 July 2026

Students Enrolled

207

Chat on WhatsApp

Quick reply guaranteed

Chat Now+91 89039 00752

Key Features

Real-time object detection
Obstacle identification
Object position detection
OCR-based text reading
Text-to-Speech audio guidance
Emergency SOS alert
Wearable smart spectacles
AI-powered navigation assistance
Portable design

Technologies Used

PythonRaspberry Pi 4OpenCVTesseract OCRText-to-Speech (TTS)Raspberry Pi OSComputer VisionArtificial IntelligencePi Camera Module

Project Modules

Camera Module

Complete camera module module with full implementation

Image Processing Module

Complete image processing module module with full implementation

Object Detection Module

Complete object detection module module with full implementation

OCR Module

Complete ocr module module with full implementation

Audio Feedback Module

Complete audio feedback module module with full implementation

Emergency SOS Module

Complete emergency sos module module with full implementation

Raspberry Pi Controller

Complete raspberry pi controller module with full implementation

Blynk Communication Module

Complete blynk communication module module with full implementation

User Interface Module

Complete user interface module module with full implementation

Hardware Requirements

Processor

Raspberry Pi 4 Model B

Camera

Raspberry Pi Camera Module

Audio Output

Earphones / Speaker

Input Device

Push Button (SOS)

Storage

64GB Micro SD Card

Power Supply

5V USB-C Power Adapter / Power Bank

Software Requirements

Operating System

Raspberry Pi OS (Linux)

Programming Language

Python 3.x

Object Detection

YOLOv4-Tiny

Computer Vision

OpenCV

Speech Engine

Text-to-Speech (TTS) Engine

IoT Platform

Blynk Platform

Frequently Asked Questions

What is the purpose of this project?

To assist visually impaired individuals by providing real-time navigation, object detection, text reading, and emergency assistance.

Which AI model is used?

YOLOv4-Tiny is used for real-time object detection.

How does the system read text?

It uses EasyOCR or Tesseract OCR to extract text from captured images.

Does the project provide voice assistance?

Yes. The extracted information is converted into speech using a Text-to-Speech engine.

Is this suitable as a final year engineering project?

Yes. It is suitable for CSE, ECE, AI & ML, IT, EEE, and Biomedical Engineering students.

Student Reviews

“As an MCA student, I needed a full-stack project. CodeSelf delivered an excellent web application with proper documentation and live demo.”

A

Anjali Mehta

MCA, COEP Pune

“Great support and quality projects. The Raspberry Pi project worked perfectly and the documentation was thorough. Highly recommended.”

V

Vikram Singh

EEE, Delhi Technological University

“The IEEE project I ordered was exactly what I needed. The team helped me understand the implementation and I scored well in my viva.”

S

Sneha Reddy

CSE, VIT Vellore

“Best project center for engineering students. The MERN stack project I purchased came with clean code, proper documentation, and modifications were done for free.”

A

Arun Kumar

IT, PSG College of Technology

“I was struggling with my IoT project until I found CodeSelf. The project was delivered instantly and the demo video made it easy to understand.”

P

Priya Patel

ECE, RV College of Engineering

“Excellent platform for final year projects. Got my AI/ML project with complete documentation and source code. The viva support was really helpful.”

R

Rahul Sharma

CSE, BMS College of Engineering

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