Vocalization sign language with deep learning.
In this project we use our own Sign Language Dataset
.
Vocalization sign language iOS App: Vocalization-Sign-Language-iOS
Demo |
---|
![]() |
Watch Demo Videos 🔊 |
- Getting Dataset
- Artificial Intelligence Model Accuracy
- Model Architecture
- Model Training
- Using Tensorboard
- Creating Dataset
- This project works best in the white background and good light.
In this project, I added deep learning to my old lip reading project SesimVar(Turkish).
Note: If you are failed, look up For Development
title in bellow.
python3 live.py
Note: If you want, you can change the delay time.
python3 predict.py <ImageFileName>
cd Data && chmod +x download_dataset.sh && ./download_dataset.sh
Demo |
---|
![]() |
Watch Demo Videos 🔊 |
DataSet Examples | Model Accuracy |
---|---|
![]() |
![]() |
In this project we use our own Sign Language Digits Dataset.
At the end of 25 epochs, 97% accuracy was achieved in the test without data augmentation:
At the end of 25 epochs, 99% accuracy was achieved in the test with data augmentation:
-
Input Data Shape: 64x64x1
-
Convolutional Layer 32 filter Filter shape: 3x3 Strides: 1x1 Padding: Same
-
Activation Function: ReLu
-
Convolutional Layer 64 filter Filter shape: 3x3 Strides: 1x1 Padding: Same
-
Activation Function: ReLu
-
Max Pooling Pool shape: 2x2 Strides: 2x2
-
Convolutional Layer 64 filter Filter shape: 3x3 Strides: 1x1 Padding: Same
-
Activation Function: ReLu
-
Max Pooling Pool shape: 2x2 Strides: 2x2
-
Convolutional Layer 128 filter Filter shape: 3x3 Strides: 1x1 Padding: Same
-
Activation Function: ReLu
-
Max Pooling Pool shape: 2x2 Strides: 2x2
-
Flatten
-
Dense Size: 526
-
Activation Function: ReLu
-
Dropout Rate: 0.5
-
Dense Size: 128
-
Activation Function: ReLu
-
Dropout Rate: 0.5
-
Dense Size: Class size in dataset
-
Activation Function: Softmax
Total params: 4,507,864
Trainable params: 4,507,864
Non-trainable params: 0
python3 train.py
Not forget to download dataset before training!
tensorboard --logdir=Data/Checkpoints/logs
For getting dataset look up Getting Dataset title in this file.
For your own dataset:
- Create 'Data/Train_Data' folder.
- Create folder in 'Data/Train_Data' folder and rename what you want to add char or string.
- In your created char or string named folder add much photos about created char or string named folder. Note: We work on 64x64 image also if you use bigger, program will automatically return to 64x64.
- Used Python Version: 3.6.0
- Install necessary modules with
sudo pip3 install -r requirements.txt
command. - Install OpenCV (We use version: 3.2.0-dev)