Handwritten Mathematical Expression Recognition and Grading System

Abstract

Handwritten Mathematical Expression recognition and grading system is a challenging task in the field of pattern recognition. A lot of researchers have already worked on Handwritten Mathematical Expression recognition and have used various classifiers. In past, Convolutional Neural Network, also called CNN, has been highly used for recognizing patterns. In this paper, We propose an idea to recognize HME and evaluate offline using CNN for classification. The steps included are, first the worksheet is scanned and is sent to the work-spaces detection module where it will return all the rectangular work-spaces from the given worksheet, then the workspaces are sent to the line extraction module to extract all the lines. The extracted lines are then passed to the character segmentation module where it will segment the character and then characters will be classified using deep learning model DCCNN. Finally, the evaluation module will assess the line and draw a green/red bounding box depending on whether the line is correct or not.