Convolutional neural networks applied to handwritten mathematical symbols classification

Convolutional Neural Networks have achieved great performance in computer vision and pattern recognition applications. In this work, we propose the use of Convolutional Neural Networks in order to improve the handwritten mathematical symbol classification rate. We trained a Convolutional Neural Networks to classifying mathematical symbols into a proper class using Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 dataset. The results show that Convolutional Neural Networks outperforms the previous works in terms of accuracy.