Methodology for accurate extraction of scientific charts
The following procedure is recommended to ensure a lossless transformation of data visualization in academic literature:
- Pre-processing norms1) Screenshots retain axis scale labels 2) Legend boxes need to be included for line graphs 3) Convert color graphs to grayscale distinguishable mode first
- Model-specific optimization: 1) Integration of Plotly format converter 2) Support for extraction of raw data points (
extract_raw_data=True(3) Automatic derivation of coordinate system mapping relationships - output control:: 1) Form class: generated by default
pandas.DataFrameReadable structure 2) Flowchart: to SVG vector format 3) Box-and-line diagram: parsing five-number generalized data
Quality inspection methods: 1) Enablevisual_validation=TrueGenerate comparative charts 2) Use two-model cross-validation for important charts 3) Do diff analysis with results from tools like Tabula
This answer comes from the articleSmolDocling: a visual language model for efficient document processing in a small volumeThe
May not be reproduced without permission:AI productivity tools " How to achieve high fidelity extraction of graphical data in academic papers?
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