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%.