Bangla sign recognition system is developed based on a deep learning approach named LSTM. As a feature extractor, Mediapipe Holistic is also used in this system. This system is able to work on static and dynamic both types of gestures and recognize simple and complex signs.
Mediapipe Holistic extracts feature from a person's face, body, hand, and finger positions. Mediapipe Holistic uses specific points to monitor our facial expressions and hand movements. Mediapipe Holistic generates landmarks to detect the face, body, and hand. As a result, redundant information is automatically removed from the picture or frame of a video. This system saves space because it only requires a few points to be saved. It does not introduce displacement due to lighting or distance.
Recurrent Neural Networks in particular may learn long-term dependencies. Unlike conventional convolutional neural networks, LSTM has backpropagation. A forget gate, a cell, an input gate, and an output gate make up a basic LSTM unit. Three gates regulate the flow of data into and out of the cell, which also has a long-term storage capacity. LSTMs do a great job of categorizing, evaluating, and generating predictions based on time series data because key events in a time series may have unexpected latency. In Bangla sign language, a complex sign consists of several patterns and gestures. There is almost always a connection between recent and previous moves. In order to remember the previous one for a while, the LSTM architecture was used.
