download and save the model.h5 file from the following link in the same directory before running the code https://mavsuta-my.sharepoint.com/:u:/r/personal/vxc0340_mavs_uta_edu/Documents/model.h5?csf=1&web=1&e=Y2ynAj
Our aim for developing this research is that it may be deployed as a mobile application in the future for persons who cannot speak, acting as a transulator. since these are the early stages of implementation, yet these are helpful concepts So far, we've improved our accuracy while training. When we tested the program on a simple background, it worked well, but when there is a lot of noise, such as background objects, it takes time and some prediction, and we also have to consider that we have to distinguish 26 different signs, rather than simply 1 or 2 faces in a face recognition thats so challenging.
- python v3.10.x
- gTTS v2.2.4
- keras v2.8.0
- matplotlib v3.5.3
- nltk v3.7
- numpy v1.22.4
- opencv_python_headless v4.6.0.66
- pyttsx3 v2.90
- scikit_learn v1.1.3
- tensorflow v2.8.2
- textblob v0.17.1
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before running the code unzip all the compressed files.
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Install python v3.10.x if not present already.
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Install dependencies from requirements.txt file using command below:
pip install requirements.txt -
Run the jupyter notebook
app.ipynband run it to test the model.