Extracting Semantic and Context Information from Images and Equations

A vast majority of academic content includes information that is locked in images, equations, and symbols. The challenging problem of extracting semantic and contextual information from images and equations is very closely related to the problem of automated ingestion of content from unstructured data sources. Extracting semantic information from images is still a domain-dependent hard task requiring large datasets, complex machine vision, and deep learning approaches.

Extracting meaningful information from images and equations is a domain-dependent task requiring a comprehensive understanding of the underlying academic content. Images in academic literature can encompass various visual representations, including line-based geometric diagrams, chemical structures, mathematical formulas, graphs, colourful illustrations, and intricate flowcharts. Each visual element holds crucial semantic and contextual information that needs to be deciphered.

The process of extracting semantic and contextual information from academic images and equations necessitates the use of complex machine vision techniques. Initially, a vast and diverse dataset must be curated, encompassing a broad spectrum of academic content. Next, machine vision models, such as VIT (Vision Transformer), are trained on this dataset using advanced deep learning techniques. These models learn to extract features, recognize patterns, and establish associations between visual elements and their semantic meaning.