The main aim of our "Smart Text Detection Glass" is to help visually impaired people to read the text. It provides the solution for them to complete even education despite all the difficulties faced by them. Our project is divided into four sections they are Text detection, Text recognition, Text to Speech Conversion and text translation. The task of our glass is to scan any text image and convert it into audio text so the audio can be listened to by the user through headphones. The technologies we are going to use in this project are OCR, Google Text to Speech. The text detection from images is done by using OpenCV and Optical Character Recognition with Tesseract OCR Engine.OpenCV is a real-time computer vision programming functioning library that is used for the digital image or video processing. Optical Character Recognition is the technology in which the Handwritten, typed or printed text into machine-encoded text. The OCR process involves five stages they are preprocessing, image segmentation, feature extraction, image classification and post-processing. We are also using an Efficient and Accurate Scene Text Detector (EAST) which uses Convolutional Neural Network Algorithm. Efficient And Accurate Scene Text Detector is a method and also simple and powerful technology that allows detecting a text in natural scenarios with high accuracy and efficiency. As we are converting the text to audio we are using Text to speech technology by using gTTS library. Now the text is translated to Tamil by using Google Translation Services Library. All the software part is done by using Python compiler. In that case, we are using IDLE with Python 3.9.7. We are embedding this software with hardware by using Raspberry PI Model 3b+. Our Prototype plan includes the glasses with a webcam and headphones. The Raspberry pi is fitted on the user's arm. The Raspberry pi is fitted with a push-button in it. When the user presses the push button the picture is captured in the webcam fitted in the glass frame. The image is then processed and text characters present in the image is extracted and then translated the text to Tamil . Finally the translated text is converted into audio output which will be heard in the headphones. In this work we planning to implement the EAST convolutional neural network algorithm in FPGA hardware as it involves more number of numerical calculations and it is time consuming one if it done in sequential manner. So we prefer FPGA to perform this task.

Project Proposal

1. High-level project introduction and performance expectation

2. Block Diagram

3. Expected sustainability results, projected resource savings