Problem Statement
The current ASL recognition model performs gesture detection effectively in controlled environments, but prediction accuracy may decrease under real-world conditions such as:
- Different lighting environments
- Hand orientation variations
- Camera distance changes
- Background noise
- Partial hand visibility
This can lead to unstable or incorrect gesture predictions during real-time usage, reducing the reliability and usability of the system.
Proposed Solution
Introduce a robust data augmentation pipeline during model training to improve the generalization capability of the ASL recognition model. The goal is to expose the model to diverse variations of hand gestures so it can perform more accurately across different real-world scenarios.
Key Features
Advanced Data Augmentation
Apply augmentation techniques such as:
- Rotation
- Zoom
- Horizontal shifting
- Brightness adjustment
- Flipping
- Noise injection
Improved Generalization
Train the model on more diverse samples to reduce overfitting and improve real-time prediction performance.
Better Real-Time Stability
Reduce sudden prediction fluctuations caused by:
- Hand movement
- Lighting changes
- Webcam quality variations
Training Performance Comparison
Add evaluation metrics to compare:
- Accuracy before augmentation
- Accuracy after augmentation
- Validation loss improvements
Scalable Training Pipeline
Create a modular augmentation pipeline that can easily support future datasets and model upgrades.
Technical Suggestions
- Use TensorFlow/Keras ImageDataGenerator or tf.image augmentation pipeline
- Add preprocessing normalization
- Introduce batch-wise augmentation during training
- Compare model performance using confusion matrix and accuracy graphs
- Maintain compatibility with the existing ASL recognition architecture
Benefits
- Improves prediction accuracy
- Enhances robustness in real-world usage
- Reduces model overfitting
- Creates smoother real-time gesture detection
- Makes the project more production-ready
Future Scope
- Dynamic gesture augmentation
- Synthetic gesture generation
- Adaptive training for different users
- Low-light ASL recognition optimization
- Mobile-device optimized inference
Contribution Interest
I would love to contribute to this feature under GSSoC'26 by working on:
- Data augmentation pipeline
- Model retraining and evaluation
- Accuracy comparison experiments
- Training visualization improvements
- Real-time prediction stability enhancements
Looking forward to contributing to this impactful accessibility-focused project 🚀
Problem Statement
The current ASL recognition model performs gesture detection effectively in controlled environments, but prediction accuracy may decrease under real-world conditions such as:
This can lead to unstable or incorrect gesture predictions during real-time usage, reducing the reliability and usability of the system.
Proposed Solution
Introduce a robust data augmentation pipeline during model training to improve the generalization capability of the ASL recognition model. The goal is to expose the model to diverse variations of hand gestures so it can perform more accurately across different real-world scenarios.
Key Features
Advanced Data Augmentation
Apply augmentation techniques such as:
Improved Generalization
Train the model on more diverse samples to reduce overfitting and improve real-time prediction performance.
Better Real-Time Stability
Reduce sudden prediction fluctuations caused by:
Training Performance Comparison
Add evaluation metrics to compare:
Scalable Training Pipeline
Create a modular augmentation pipeline that can easily support future datasets and model upgrades.
Technical Suggestions
Benefits
Future Scope
Contribution Interest
I would love to contribute to this feature under GSSoC'26 by working on:
Looking forward to contributing to this impactful accessibility-focused project 🚀