Towards a low-cost air-written character recognition system

Pronk, P.J.

Towards a low-cost air-written character recognition system

Designing an embedded machine learning system to recognise the first 10 letters of the Latin alphabet

Bachelor thesis (2023)

Authors

P.J. Pronk Electrical Engineering, Mathematics and Computer Science

Contributors

Q. Wang EEMS - General (mentor)

M. Yang Embedded Systems - (coach)

R. Zhu Embedded Systems - (coach)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Neural network Convolutional neural network TensorFlow Lite for Microcontrollers Arduino Nano 33 BLE TinyML Gesture Recognition Embedded Deep Learning Touch-Free Interaction Air Writing Visible Light

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http://resolver.tudelft.nl/uuid:0dbeb7e6-89bd-41e5-bf1d-f99363702551

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Published Date

25-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This study introduces a novel system that leverages three photodiodes and ambient light to identify air-written characters on a resource-constrained device. Through experimentation, suitable methods of data preprocessing, machine learning and model compression were selected to recognize the first 10 characters of the Latin alphabet. The final system was able to recognize these characters with a between-participant accuracy of 50.80% and a within-participant accuracy of 67.82%.

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