This paper describes the feasibility of digit detection using three photodiodes and an Arduino Nano 33 BLE. This is done using a controlled lighting condition, using a bright lamp. It dives into the process of data collection, preprocessing and model selection for a recurrent neural network to do the classification of gestures into digits. Using a ”ConvLSTM double conv. layer model” with 128 units we were able to achieve an average accuracy of 0.500 ± 0.091 on a 5-Fold cross-validation procedure based on data that was collected in a controlled lighting environment. While this provides a foundation for digit detection using time-series data in a controlled light environment, further suggestions are made for future improvements and expansions in this area.