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rohanladhani / repository
The Sign Language Interpreter project uses a CNN model to predict letters from images or webcam input, enhancing communication for the deaf and hard of hearing. It features an intuitive interface built with Streamlit, supported by a robust backend of Python, FastAPI, and Jupyter Notebook, promoting inclusivity through innovative technology.
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The Sign Language Interpreter project enhances communication between individuals who are deaf or hard of hearing and those who use spoken language. Utilizing a Convolutional Neural Network (CNN) model, this project predicts letters from images or webcam input, offering an intuitive interface. Streamlit ensures a seamless frontend experience, while Python, FastAPI, and Jupyter Notebook form the robust backend. This initiative promotes inclusivity and exemplifies the integration of innovative machine learning techniques with user-centric design.
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TheMadrasTechie /
This is a jupyter notebook program to train and detect the hand gestures of american signlanguage using LSTM and Mediapipe
72/100 healthspirosbax /
A collection of jupyter notebooks about Signal Processing using the Julia language.
28/100 healthsign-language-translator /
Jupyter notebooks using the sign_language_translator package and showing demos as well.
its-deepakdixit /
The project is to help the society of deprived people who are facing difficulties to speaking and hearing. Python (3.7.4), cv2 (openCV) (version 3.4.2), numpy, cvzone, Jupyter notebook
28/100 healthlucasjellema /
In this repository Jupyter Notebooks (Python, Pandas) that determine the language of documents by looking at and comparing tell tale signs, letter frequency and series analysis
28/100 healthThe-Assembly /
In this session, we’ll build a solution that detects American Sign Language (ASL) gestures via a webcam and translates them into written English in real-time via a neural network. To achieve this, we’ll mashup a few different libraries and tools and show you how to use each - starting with OpenCV & Python once again to procure live images and build our own labelled gesture data set. Following this, we’ll train and test our model on TensorFlow through transfer learning (using SSD MobileNet), during the course of which we’ll show you how to use the TensorFlow Object Detection API. Once the model is ready, we’ll plug it back into Python and OpenCV to classify based on the real-time live feed from the webcam. Prerequisites: ✅ Jupyter Notebook (https://jupyter.org/install) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #AmericanSignLanguage #TensorFlow #MachineLearning
27/100 health