Towards a low-cost air-written character recognition system

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

Pronk, Paco (TU Delft Electrical Engineering, Mathematics and Computer Science)

Wang, Q. (mentor)
Yang, M. (graduation committee)
Zhu, R. (graduation committee)

Delft University of Technology

Computer Engineering

2023-06-25

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%.

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

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

Student theses

bachelor thesis

© 2023 Paco Pronk