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"Sign Language Recognition" is a project that employs Long Short-Term Memory (LSTM) neural networks to accurately recognize and interpret sign language gestures. The project encompasses the creation of a robust dataset containing over 50 words in sign language, providing a diverse range of gestures for training and testing the model.

By utilizing LSTM, a type of recurrent neural network (RNN), the project capitalizes on the network's ability to process sequential data and capture temporal dependencies. This allows the model to effectively understand and interpret the nuanced movements and patterns inherent in sign language gestures.

To enhance the model's performance, hyperparameters have been fine-tuned through rigorous experimentation. The tuning process involves optimizing parameters such as the learning rate, batch size, number of LSTM layers, and hidden units, among others. By selecting the optimal combination of hyperparameters, the model achieves improved accuracy and reliability in recognizing sign language gestures.

Additionally, the project leverages the OpenCV library, which provides computer vision capabilities, to capture and preprocess video input. OpenCV aids in extracting relevant features from the video frames, such as hand movements and positions, which are then fed into the LSTM model for recognition and interpretation.