# Sign Language Recognition Model

This model recognizes sign language gestures using landmark data from hand, pose, and face keypoints.

## Model Details

- **Model Type**: Sign Language Recognition
- **Framework**: TensorFlow/Keras
- **Input**: Landmark sequences (x, y, z coordinates)
- **Output**: Sign language class predictions
- **Classes**: 60 different signs
- **Parameters**: 1763418

## Model Architecture

- **Input Shape**: (,384,708)
- **Output Shape**: (,60)
- **Max Sequence Length**: (384)
- **Embedding Dimension**: (192)

## Training Details

- **Epochs**: 69
- **Batch Size**: 32
- **Learning Rate**: 0.0005
- **Weight Decay**: 0.1
- **Best Validation Loss**: 3.1850430965423584
- **Best Validation Accuracy**: 0.25550660490989685

## Usage

```python
import tensorflow as tf
import pickle
import numpy as np

# Load the model
model = tf.keras.models.load_model('model.h5')

# Load the processor
with open('processor.pkl', 'rb') as f:
    processor = pickle.load(f)

# Example inference
# your_landmark_data should be preprocessed using the same processor
predictions = model.predict(your_landmark_data)
predicted_classes = np.argmax(predictions, axis=1)
```

## Files Description

- `model.h5`: Complete Keras model (recommended for inference)
- `model_weights.h5`: Model weights only
- `processor.pkl`: Data processor for landmark preprocessing
- `config.json`: Model configuration and metadata
- `training_history.json`: Training metrics and history
- `inference_example.py`: Example inference script
- `requirements.txt`: Required dependencies

## Requirements

See `requirements.txt` for the complete list of dependencies.

## Training Notebook

The training notebook will be provided in future updates