Machine learning models for mathematical symbol recognition: A stem to stern literature analysis

• Published in: Multimedia tools and applications Vol. 81; no. 20; pp. 28651 - 28687
• Main Authors: Kukreja, Vinay, Sakshi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2022; Springer Nature B.V
• Subjects: Accuracy; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Domains; Feature extraction; Handwriting recognition; Literature reviews; Machine learning; Mathematical analysis; Multimedia Information Systems; Optical character recognition; Segmentation; Special Purpose and Application-Based Systems; Support vector machines; Symbols; Handwritten mathematical symbol recognition; Segmentation; Optical character recognition; Convolutional neural network; Machine learning; Mathematical symbol recognition; Support vector machine
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12644-2

Given the ubiquity of handwriting and mathematical content in human transactions, machine recognition of handwritten mathematical text and symbols has become a domain of great practical scope and significance. Recognition of mathematical expression (ME) has remained a challenging and emerging research domain, with mathematical symbol recognition (MSR) as a requisite step in the entire recognition process. Many variations in writing styles and existing dissimilarities among the wide range of symbols and recurring characters make the recognition tasks strenuous even for Optical Character Recognition. The past decade has witnessed the emergence of recognition techniques and the peaking interest of several researchers in this evolving domain. In light of the current research status associated with recognizing handwritten math symbols, a systematic review of the literature seems timely. This article seeks to provide a complete systematic analysis of recognition techniques, models, datasets, sub-stages, accuracy metrics, and accuracy details in an extracted form as described in the literature. A systematic literature review conducted in this study includes pragmatic studies until the year 2021, and the analysis reveals Support Vector Machine (SVM) to be the most dominating recognition technique and symbol recognition rate to be most frequently deployed accuracy measure and other interesting results in terms of segmentation, feature extraction and datasets involved are vividly represented. The statistics of mathematical symbols-related papers are shown, and open problems are identified for more advanced research. Our study focused on the key points of earlier research, present work, and the future direction of MSR.