Machine learning and non-machine learning methods in mathematical recognition systems: Two decades’ systematic literature review

Tools based on machine learning (ML) have seen widespread application in the prediction and categorization of mathematical symbols and phrases. The purpose of this work is to conduct a comprehensive analysis of the machine learning and non-machine learning strategies that are currently in use for the recognition of mathematical expressions. (MEs). The authors collected and analyzed research studies on the recognition of MEs (and closely related models and issues as well), which are published from January 2000 to December 2022 in the SLR. The review has nominated 98 primary studies out of the extracted 202 studies after heedful filtering using inclusion/exclusion criteria and quality assessment. The pertinent data is derived from IEEE explore, Science Direct, Wiley, Scopus, ACM Digital Library, etc. For assiduously reviewing and synthesizing the data, the authors used grounded theory and other qualitative and quantitative techniques. The analysis reveals that the support vector machine as an ML model with CROHME as the dataset and expression recognition rate as an accuracy metric is frequently used in the chosen studies. Recognition is typically fragmented down into three stages—segmenting symbols, recognizing symbols, and analyzing structures—in non-ML studies. In conclusion, this work aims to synthesize the results of existing research to provide a summary of the state-of-the-art in recognizing handwritten MEs.