Sign Language Classification
18/08/2024
Project Description
The Sign Language Classification project is aimed at building a machine learning model that can recognize and classify different sign language gestures. The project leverages computer vision techniques to interpret hand movements and convert them into meaningful symbols or words.
Key Steps
Data Collection: Collect a large dataset of images or videos showing different sign language gestures. Use labeled data to map gestures to corresponding letters, words, or phrases. Data Preprocessing: Convert images/videos into usable formats (e.g., resizing, normalization). Use techniques like grayscale conversion, image augmentation, and filtering to enhance data quality and variety. Model Building: Use Convolutional Neural Networks (CNNs) to build a model capable of identifying and classifying sign language gestures. Split the dataset into training, validation, and test sets to ensure robust model performance. Model Evaluation: Evaluate the model's accuracy using metrics such as confusion matrix, precision, recall, and F1-score. Apply techniques like cross-validation and hyperparameter tuning to improve model performance. Real-Time Prediction: Implement real-time gesture recognition using a camera feed. Integrate the model into an application that can recognize and translate sign language gestures as they happen. Deployment: Deploy the model in a web or mobile application to make sign language interpretation more accessible in real-world scenarios.
Technologies Used
Programming Languages: Python Libraries: TensorFlow, Keras, OpenCV, Scikit-learn, NumPy, Pandas Model: Convolutional Neural Networks (CNNs) Tools: Jupyter Notebook, Flask/Django (for deployment)
