The project of graduation essay.
In this work, we have applied Deep Learning for sign language recognition. VGG16, RNN and LSTM were used.
Member
- Vu Truong Giang
- Tat Tran Phong
We use LSA64: A Dataset for Argentinian Sign Language
It is available on Link.
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VGG16 + LSTM method:
VGG16_LSTM_Train : file for training by LSTM model
CM_VGG16_LSTM_Test : file for testing LSTM model on test set
VGG16 + RNN method:
VGG16_RNN_Train : file for training by RNN model
CM_VGG16_RNN_Test : file for testing RNN model on test set
| Index | Name | Accuracy |
|---|---|---|
| 1 | VGG16 + RNN | 82.81% |
| 2 | VGG16 + LSTM | 95.62% |
Library: NumPy, os, Matplotlib, Tensorflow, Keras, Sklearn, opencv, Pandas, Seaborn
LSTM model
- Run file VGG16_LSTM_Train.ipynb to train data and create weights.
- Run file CM_VGG16_LSTM_Test.ipynb for testing model and showing result.
RNN model
- Run file VGG16_RNN_Train.ipynb to train data and create weights.
- Run file CM_VGG16_RNN_Test.ipynb for testing model and showing result.