sign language reader

Open CV and machine learning algorithm based sign language reader system will become a language translator system which can be used by mute or impaired persons who cannot speak. The system consist of  a web camera and transfer corresponding Sign to the raspberry pi. Raspberry pi act as a brain

Project Title

sign language reader

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Open CV and machine learning algorithm based sign language reader system will become a language translator system which can be used by mute or impaired persons who cannot speak. The system consist of  a web camera and transfer corresponding Sign to the raspberry pi. Raspberry pi act as a brain of the system depending on the operation of the device. This detect the sign gesture from the hand of the person and convert it in to a text in English.

This process is implemented when a mute person communicate with normal person. In this mode the web camera snaps the gestures Sign and transferred it into a Raspberry PI. The open CV library on the Raspberry PI process the gesture and produce as corresponding text output which is displayed on LCD Module.

Project Objectives

• To design a system that detect hands and record the hands gestures.
• To implement the system in real time.
• Use tensorflow to store and analyze the data of hand movements.
• To detect a gesture and convert it into English text words.

Project Implementation Method

• Interface the web camera with the Raspberry Pi using appropriate wires.
• Open the Raspbian OS on your PC/ laptop.
• Type the code for continuous video recording in Python.
• Now you will use OpenCV to detect hands and record the hand gestures.
• Use tensorflow to store and analyse the hand movements. You have to give meaning to every hand movement. You also have to give it the functionality of understanding facial features. Based on this, the system can detect changes in facial expression.
• The machine has to learn the meanings of different hand gestures. For this, you have to train it by recording different gestures and giving it meaning.
• Once it detects a gesture, it should convert it into words or phrases.
• It will become better at predicting hand gestures with time and practice.

Benefits of the Project

• My project is implement on school, where normall teacher adress to the student, toward the mute students.
• It will also implement on a side where community effect by language barrier.

Technical Details of Final Deliverable

• unknown speech is compared against the set of pe recorded words.
• an experrt knowledge about variations in speech is hand codded in to a system.
• the artificail intelligence approach attemots to mechamize the recognition procedure and finally making a decision on the measeured features.
• it convert the hand gestures input to the text output.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Education

Other Industries

Manufacturing

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Quality Education, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com