# MatGD: Materials Graph Digitizer

arXiv: https://arxiv.org/abs/2311.12806 · HTML: https://mlsanity.com/paper/2311.12806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks](https://mlsanity.com/paper/2106.13327/) (2021)
- [Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing](https://mlsanity.com/paper/2101.01508/) (2021)
- [Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3](https://mlsanity.com/paper/1910.07234/) (2019)

## Built on by (most cited first, 4 of 4)

- [Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks](https://mlsanity.com/paper/2407.09514/) (2024)
- [Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval](https://mlsanity.com/paper/2407.15459/) (2024)
- [Beyond Text and Tables: Vision-Language Model Integration in ComProScanner for Extracting Materials Data from Scientific Figures with High Accuracy](https://mlsanity.com/paper/2606.00065/) (2026)
- [Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties](https://mlsanity.com/paper/2604.19383/) (2026)

## Abstract (arXiv)

We have developed MatGD (Material Graph Digitizer), which is a tool for digitizing a data line from scientific graphs. The algorithm behind the tool consists of four steps: (1) identifying graphs within subfigures, (2) separating axes and data sections, (3) discerning the data lines by eliminating irrelevant graph objects and matching with the legend, and (4) data extraction and saving. From the 62,534 papers in the areas of batteries, catalysis, and MOFs, 501,045 figures were mined. Remarkably, our tool showcased performance with over 99% accuracy in legend marker and text detection. Moreover, its capability for data line separation stood at 66%, which is much higher compared to other existing figure mining tools. We believe that this tool will be integral to collecting both past and future data from publications, and these data can be used to train various machine learning models that can enhance material predictions and new materials discovery.