Sign Language Recognition Through Action Detection

The use of signs detection is essential for bridging the communication separation between the general population and those who are hard of hearing. This work introduces a new method for recognising sign language that combines Python-based the LSTM algorithm deep learning models with action recognition. The proposed system aims to accurately identify and decode sign language motions in real time. This research aims to efficiently recognise sign language gestures and document temporal relationships using deep learning methods, particularly the LSTM architecture. In order to train and assess the LSTM model's correctness, a comprehensive dataset comprising a range of sign language motions has been collected and analysed. Media acquiring, the pre-processing phase, extraction of features, and model training are the steps in the sign language detection pipeline. The obtained video data is divided into individual frames during the pre-processing phase, and different image processing methods are used to improve the quality and eliminate noise. The pre-processed frames are then subjected to robust feature extraction utilising methods such as machine learning-based feature extraction or optical flow. The time-dependent characteristics of sign language motions are then taught to the LSTM model using the features that were retrieved. In order to utilise pre-trained models on extensive action recognition datasets, transfer learning is also being investigated. A wide range of metrics, such as recall, precision, accuracy, and F1-score, are used to evaluate the performance of the trained model. The excellent accuracy rate attained by the LSTM based model for deep learning demonstrates its ability to manage the temporal aspect of sign language. Because of its real-time performance, the system can be used in a variety of applications, including assistive devices for people with hearing impairments or real-time interpretation tools.