Abstract
Technology is evolving at an immense speed every day. In the lap of technology, computer vision and machine learning are also growing fast. Many real time applications are running without human interaction just because of Computer vision and machine learning. In this paper, we are using computer vision and machine learning for lip feature extraction for Gujarati language. For this task we have created dataset GVLetters for Gujarati alphabets. We have taken videos of 24 speakers for 33 alphabets of Guajarati language. Face landmark algorithm from dlib is used for deriving ViLiDEx (Vibhavari’s algorithm for Lip Detection and Extraction). ViLiDEx is applied for 24 speakers and 5 alphabets from each class (Guttural, Palatal, Retroflex, Dental and Labial). This algorithm calculates total number of frames for each speaker, keep 20/25 frames as a dataset and removes extra frames. Depending on number of frames, frame numbers divisible by prime numbers are chosen for removal.
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