Communication is imperative to human existence and pervasive in all aspects of our lives. It is also an important right to everyone. This project focuses on solving the communication problem with the deaf and mute population of the world. This is done using real-time machine learning (ML) fo
ML-BASED HAND SIGN DETECTION SYSTEM FOR DEAF-MUTE PEOPLE
Communication is imperative to human existence and pervasive in all aspects of our lives. It is also an important right to everyone. This project focuses on solving the communication problem with the deaf and mute population of the world. This is done using real-time machine learning (ML) for sign language detection along with certain APIs (Application Programming Interfaces). The discussed system detects the signs in the region of interest (ROI) and then converts them into the appropriate format. Computer vision has been used for detection and API function for many other functionalities. This idea here is focused on reducing the communication gap within the deaf-mute population and bringing a sense of normal in their daily life. INTRODUCTION Communication is a way of exchanging information, opinions, and feelings among individuals in order to reach a common understanding. People with disabilities may require assistance due to complex communication needs, but they are still left behind in communicating their ideas to the general public to some extent because the majority of people are not educated enough to understand their first language, resulting in one-way communication. It's easy to be scared by the thought of communicating with someone who has a disability, particularly if you're not sure what to say or how to say it. As a result, we hope to use artificial intelligence and other technologies in our project to bridge these gaps and bring everyone, special or not, on the same level, resulting in a better world. For the general public also this application will be a very useful platform as it will completely demolish the different prerequisites that were required earlier to communicate to people with different special needs. The transmitter and the receiver are the two basic components of any communication. The sender expresses a sentiment or emotion, seeks information, or transmits an idea or notion, and the recipient receives the message.
To use artificial intelligence and other technologies in our project to bridge these gaps and bring everyone, special or not, on the same level, resulting in a better world.
For the general public also this application will be a very useful platform as it will completely demolish the different prerequisites that were required earlier to communicate to people with different special needs.
Project implementation method will be like:
System Design.
Dataset Creation .
Training and Testing.
Storing the model in cloud bucket and website integration.
Following are the benefits :
Reduces communication gap with deaf-mute population.
No need for assistance of other people to convey messages which are complex to interpret.
Helps deaf-mute people enjoy there daily and work life.
In this age where nothing seems to be impossible, leaving a section of our society out just because of their
physical disability is unfair to them. Thus our project is a small step to bridge this gap. Although we have a
lot of bases to cover, still this will hopefully start a process that might go on further ahead. With the use of
object detection, we have made the model very easy to recreate and expand or improve wherever required.
In the future, we can train our model to capture more complex live
hand signs that require more than one gesture. Also, we plan to add many other features such as performing
certain daily life actions using hand gestures or voice commands. And also this can be also incorporated
into a mobile application which would make it easier to use.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Stationary | Miscellaneous | 1 | 5000 | 5000 |
| Thesis | Miscellaneous | 1 | 5000 | 5000 |
| Epson Projector | Equipment | 1 | 45000 | 45000 |
| HP wireless printer | Equipment | 1 | 25000 | 25000 |
| Total in (Rs) | 80000 |
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