Tackling Skewness, Noise, and Broken Characters in Mathematical Expression Segmentation

Abstract

     Segmentation is one of the most computer vision processes importance, it aims to understand the image contents by partitioning it into segments that are more meaningful and easier to analyze. However, this process comes with a set of challenges including image skew, noise, and object clipping. In this paper, a solution is proposed to address the challenges encountered when using Optical Character Recognition to recognize mathematical expressions. The proposed method involves three stages: pre-processing, segmentation, and post-processing. During pre-processing, the mathematical expression image is transformed into a binary image, noise reduction techniques are applied, image component discontinuities are resolved, and skew correction is performed. Two skew correction methods are proposed: The first method is the Deskewing using iterative PCA, and the second method is the PCA prediction. The line fitting-correction image deskewing and both gave better results than the well-known Hough transformation method. In the segmentation stage, the vertical and horizontal distances between mathematical expression components are utilized to segment the components. Post-processing is employed to reassemble split symbols into a single entity. The proposed method achieves an average detection rate of 97.32%, demonstrating improved recognition outcomes for mathematical expressions.