When working with charts/schematics (using the--mode figureparameters), the VOP generates multi-layered intelligent analysis results:
1. Identification of basic elements
- Chart type judgment: Automatically differentiate line charts, bar charts, flow charts, etc.
- Key Data Point Extraction: Capture axis value ranges, legend descriptions, etc.
2. Semantic description
Example of typical output:
- "Showing a line graph of global temperature change from 2010-2020, with three trend lines for different regions."
- "Schematic diagram of cellular mitosis labeled with four stages: prophase, metaphase, metaphase, and metaphase."
3. Structured data
The JSON output contains the following fields:{
"type": "scatter_plot",
"x-axis": {"label": "Year", "range": [2000,2020]},
"series": [{"name": "Tokyo", "data": [...]}]
"description": "..."
}
This feature is particularly suitable for building AI training datasets, such as transforming schematic diagrams from biology textbooks into labeled data.
This answer comes from the articleVOP: OCR Tool for Extracting Complex Diagrams and Math FormulasThe
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