The Four Arithmetic Operations for Handwritten Digit Recognition Based On Convolutional Neural Network

Abstract: The deep learning networks has become an active area in the realm of machine learning in recent years and some relevant models such as convolutional neural network (CNN) models have a good performance in handwritten character recognition. The MNIST is a typical open source data set, which only has ten kinds of handwritten digital characters from 0 to 9. Furthermore, the majority of the open source data sets in the current literature exclude arithmetic operators of "+" , "-" , "×" , "÷" , "=". In order to address this issue, firstly, we established a data sets of 22,200 characters, which includes fifteen categories. Specially, for each category, 1,200 characters is used for training and 280 characters is used for testing respectively. Secondly, we proposed a deep learning networks algorithm for characters recognition based on CNN. Thirdly, as an application, we further implemented four arithmetic operation using the recognized characters. The experimental results demonstrate that the proposed deep learning networks algorithm has shown strong ability for feature extraction and classification with the average accuracy of 97.42 %. More meaningfully, the four arithmetic operation in the present work has strong robustness and the potential applied value.