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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Artur Antonov
Mobile Application for Translating Finger Gestures to Text
Software Engineering
(Bachelor’s programme)
9
2019
Nowadays there is a communication problem between deaf and hearing people. The reason behind this is an unpopularity of a sign language. This paper presents a possible solution to this problem – mobile application for translating finger gestures to text.

This paper describes current methods for recognizing fingerspelling gestures and new method which can run on mobile devices with limited computational power. Fingerspelling recognition method uses depth map captured by mobile device as input. Convolution neural network based on MobileNetV2 used as classifier. It was trained by using Transfer Learning technique. Training was implemented in Python 3 with Keras library and run on cloud service Paperspace.

Mobile application for translating finger gestures to text was developed for iOS operating system with Swift 4.2 programming language. Classification was integrated using CoreML and Vision libraries.

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