Recent trends in mathematical expressions recognition: An LDA-based analysis

Abstract

ContextAlthough recognition works on mathematical expressions have been explored for four decades, the current literature and trends are varied and frequently influenced by distinct emerging methods and technology. This situation instigates the necessity of an organized review to provide heedful insight into research trends and patterns currently prevailing in the domain of mathematical expression recognition (MER). ObjectiveTo identify and associate (semantic mapping) the leading research zones, core research areas, and research trends steering in the MER domain. Identifying prominent recognition models based on extracted research areas. To develop the development chart from extracted research trends for directing the future works in this direction. MethodA manual and automatic search has been performed across the reputed digital libraries for corpus formation. The formulated corpus is used for topic modeling, and Latent Dirichlet Allocation is deployed for information modeling for achieving defined objectives. ResultThe corpus of 325 research papers published from 1967 to 2021 has been processed using LDA. The five major research areas and ten research trends are identified. Leading research area is “Segmentation and Classification Procedures”, and the trend with the highest related publications is “Contextual and Graph-based recognition”. “Attention and Deep Networks” has emerged as the newborn trend, and the identified newborn, young, and matured trends impetrate more exploration from the MER research community.