An app that makes communication with the deaf and dumb smart by converting their hand gestures to text which can be understood by normal person.

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For actions to speak louder than words, Paired has been designed to enhance the communication between normal and specially abled (deaf and dumb).

• Disabled people face difficulty in communicating with each other when at distance.
• Chatting apps currently do not focus on connecting disabled people.
• Communication using Sign Language feature is also not enabled in many apps.
• Communication using the Braille feature also lacks in many chatting apps.
• Current messaging apps are also not feasible for illiterate disabled people.

• The app can recognize American Sign Languages from gestures using Machine Learning enabling Deaf and Dumb to send messages using it.
• Incoming messages will be converted from text to American Sign Language for the deaf and dumb people to recognize the messages even if they are not literate.
• Blind people can also send messages using their Braille.
• The incoming messages will also be converted to speech to enable blind people to hear the messages

1. The web-based application takes in the data from the webcam available on the laptop/phone (machine). This data stream is processed to narrow down the live data stream.

2. The data is now fed into a Cnn based machine learning model which makes a prediction on the basis of the symbols present in the video stream made by the user

3. The predictions are displayed and fed to the chat for the user

4. The Process of reading data, preprocessing the video stream and making predictions on the video is done for every time the User wants to send a message.

5. Blind people will place their fingers over the button which has been placed at convenient locations for them.

6. They will type the Braille messages which will be interpreted by the app and then the messages will be sent to the receiver in both speech and sign language form

• Django
• Image Processing
• Image classfication using classifier
• Image classification using CNN classifier
• CNN classifier build using Tensorflow

move to project root- paired

1. Create and activate a virtualenv (Python 3)

python -m venv myenv
cd myenv/Scripts
source activate
cd ../..

2. Install requirements

pip install django
pip install imageio
pip install tensorflow
pip install scikit-image
pip install cv2
pip install numpy

3. move to backend

move to pairedBackend directory

4. Start database Server

python manage.py makemigrations
python manage.py migrate

5. Create admin user

./manage.py createsuperuser (not mandatory)
enter name ,email,password ,confirm password,and press y

6. Run development server

./manage.py runserver

7. live http://127.0.0.1:8000/home/

• Arpit Srivastava
• Aman Raj
• Aniket Dixit
• Souhard Swami