Digits Recognition with Quadrant Photodiode and Convolutional Neural Network

Abstract

In this paper we have investigated the capabilities of a quadrant photodiode based gesture sensor in the recognition of digits drawn in the air. The sensor consisting of 4 active elements, 4 LEDs and a pinhole was considered as input interface for both discrete and continuous gestures. Index finger and a round pointer were used as navigating mediums for the sensor. Experiments performed with 5 volunteers allowed to record 300 examples of each digit from 0 to 9, which were drawn in the air. Digits were converted from a list of recorded coordinates into images processed as in the MNIST database. Three approaches for recognition of digits recorded by quadrant photodiode were considered: convolutional neural network trained only on examples from the MNIST database, network trained on mixed data of MNIST with examples recorded using quadrant photodiode (4/1 proportions) and trained on the MNIST with examples recorded using the elaborated sensor but after the arbitral rejection of 20% of worst quality data (4/1 proportions preserved). The application of the third approach in comparison to the first one allowed to increase the overall accuracy of digits classification from 34.4% to 86% for testing data recorded with the use of the pointer and from 32% to 81.2% for data recorded with the use of a finger (for 50Hz sampling frequency).