Data export capabilities for machine learning
Versatile OCR Program adopts a two-stage design in the data processing flow, first decomposing the original document into text/formula/table/chart elements, and then generating structured data through semantic analysis. The output format is optimized for AI training: JSON format contains complete element coordinates, type labels and semantic context; Markdown format maintains the readability of academic documents. Typical examples include converting diagrams from EJU biology papers into training data with annotations such as "micrographs showing meiosis phases", or parsing mathematical formulas into dual representations containing both LaTeX code and the description of "inequality with trigonometric functions". The tool also supports batch processing. The tool also supports batch processing, with the -input_dir parameter converting an entire library of research papers into a structured dataset at once.
This answer comes from the articleVOP: OCR Tool for Extracting Complex Diagrams and Math FormulasThe
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