Handwritten Mathematical Expression Recognition Using Convolutional Neural Network

• Published in: 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC) pp. 15 - 19
• Main Authors: Tran, Giang Son, Huynh, Chi-Kien, Le, Thanh-Sach, Phan, Tan-Phuc, Bui, Khanh-Ngoc
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2018
• Subjects: Computer science; convolutional neural network; Convolutional neural networks; Handwriting recognition; Image recognition; Layout; mathematical expression recognition; mathematical symbol detection; Training; Urban areas
• DOI: 10.1109/CRC.2018.00012

Recognizing mathematical expressions on raster images usually consists of two steps: detecting individual symbols and analyzing their spatial structure to form a coherent equation. In this work, we focus on the first step and propose a detection method that is able to locate small and difficult handwritten symbols. We use a deep convolutional neural network with robust detection performance. It is able to achieve a mean average precision score of 0.65 for 106 different mathematical symbols on the dataset we created. For structural analysis, we use the DRACULAE parser since it has high accuracy given that the symbols were correctly detected