# A Survey on Figure Classification Techniques in Scientific Documents

arXiv: https://arxiv.org/abs/2307.05694 · HTML: https://mlsanity.com/paper/2307.05694/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Data Extraction from Charts via Single Deep Neural Network](https://mlsanity.com/paper/1906.11906/) (2019)
- [Chart-Text: A Fully Automated Chart Image Descriptor](https://mlsanity.com/paper/1812.10636/) (2018)
- [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021)
- [Viziometrics: Analyzing Visual Information in the Scientific Literature](https://mlsanity.com/paper/1605.04951/) (2016)
- [SlideImages: A Dataset for Educational Image Classification](https://mlsanity.com/paper/2001.06823/) (2020)

## Built on by (most cited first, 2 of 2)

- [SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration](https://mlsanity.com/paper/2505.02418/) (2025)
- [Patent Figure Classification using Large Vision-language Models](https://mlsanity.com/paper/2501.12751/) (2025)

## Abstract (arXiv)

Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing information from figures could lead to deeper insights into the concepts highlighted in the scientific documents. In this survey paper, we systematically categorize figures into five classes - tables, photos, diagrams, maps, and plots, and subsequently present a critical review of the existing methodologies and data sets that address the problem of figure classification. Finally, we identify the current research gaps and provide possible directions for further research on figure classification.