Sign Language Recognition using Deep Learning - Matlab

Sign language recognition is a fascinating field that bridges communication gaps between hearing-impaired individuals and the rest of the population. By leveraging deep learning techniques, we can create systems that understand and interpret sign language gestures, enabling more inclusive communication. In this article, we explore how to recognize sign language using MATLAB and deep learning.

Sign language recognition involves predicting gestures, such as waving hello, sign language signs, or clapping, using a set of video frames. Unlike traditional input methods like keyboards or touchscreens, gesture recognition allows humans to interact with computers and devices naturally. Applications range from controlling consumer electronics to robot learning and computer games.

In this tutorial, we’ll cover the following steps:

1. Load Pretrained SlowFast Video Classifier:
• We’ll download a pretrained SlowFast video classifier along with a sample video file.
• SlowFast networks combine both spatial and temporal information, making them suitable for gesture recognition.
2. Classify Gestures in a Video Sequence:
• We’ll use the pretrained classifier to predict gesture labels in a video sequence.
• The class labels correspond to specific sign language gestures.

Implementation Steps

1. Load Pretrained SlowFast Video Classifier
2. Classify Gestures in a Video Sequence

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