Using TensorFlow to translate Sign Language in real-time. This model has been trained by tensorflow-K-nearest algorithm.The sign language translator is my first solo project. Its purpose is to allow users to communicate more effectively with their computers and other people. To be specific, using this program, you can sign multiple words with one gesture and copy the translated text with the click of a button.
#HOW TO DOWNLOAD FLASK AND PIP PACKAGES IN YOUR PC.
SO,at first i assume that you have installed VS CODE. INSTALL FLASK .(IF YOU HAVEN'T INSTALLED FLASK IN YOUR PC THEN FOLLOW THESE STEPS) first you in your project folder open your vs code and click on the extensions. then install python extension, also download python 3.9.6 from https://www.python.org/downloads/. and download the pip packages.' for downloading pip packages you can read the installation guide from https://pip.pypa.io/en/stable/installation/. after downloading the extensions and the python application, open the new terminal in vs code. type the command pip install flask and voila! it gets downloaded.
#HOW IT WORKS
In this real time sign language translator project,open vs code and click on the new terminal and type the command $ set flask env=development $flask run. then a local server link will get hosted and copy the given link and paste it on your url and it get working. First a User interface will get opened and after clicking on the proceed button you will get redirected to second page i.e. the main page of the app. To be specific, using this program, you can sign multiple words with one gesture and copy the translated text with the click of a button. Followed by credits page and on that there is an option for the user to check how sign language and gestures would work.
- Hand Gesture Training and Classification
- Prediction works in varying Lighting Conditions
- Retrainable Image Classes
- Translated Text can be Copied to Clipboard
- Cards that display Information about each Gesture
- Video Call functionality
- Text to Speech of translated text
- Minimal stress on memory
- Cohesive Text Styling
- Simple User Interface
- Comprehensive Commenting
#LANGUAGES USED IN THIS PROJECT. -> For Front end:- HTML,CSS,JAVASCRIPT -> For Back-end:- Python with Flask -> for functionality:- Javascript -> For training Models And App:- Tensorflow with JavaScript. -> for user-defined packages:- JSON.