A methodology for character recognition and revision of the linear equations solving procedure

Abstract

Linear equations are valuable for real-world modeling phenomena involving at least one variable. However, verifying if the procedure followed by a human for solving a linear equation was done correctly is still a complicated matter. In this paper, we propose a methodology for the automatic character recognition and revision of the solving procedure of linear equations with one unknown. First, a camera is used to acquire an image of the handwritten solving procedure. Then, the image is pre-processed, and each character and equation lines are segmented. Subsequently, a convolutional neural network (CNN) is used to conduct the character recognition stage. Finally, a comparison rule is applied to revise the solving procedure. The character recognition was verified on a 2800 image data set (2100 for training and 700 for testing), including the ten digits and four symbols: ×, +, -, /. The revision procedure was tested on a data set with 140 handwritten equations (125 for training and 15 for testing). The results revealed that we recognized handwritten characters with an accuracy of 99%, which is similar to the state-of-the-art. Moreover, our proposal revised the solving procedure with an efficiency of 86.66%.