# Chart-Text: A Fully Automated Chart Image Descriptor

arXiv: https://arxiv.org/abs/1812.10636 · HTML: https://mlsanity.com/paper/1812.10636/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 20 of 20)

- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content](https://mlsanity.com/paper/2110.04406/) (2021)
- [ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules](https://mlsanity.com/paper/2304.02173/) (2023)
- [AutoChart: A Dataset for Chart-to-Text Generation Task](https://mlsanity.com/paper/2108.06897/) (2021)
- [DOMINO: A Dual-System for Multi-step Visual Language Reasoning](https://mlsanity.com/paper/2310.02804/) (2023)
- [Towards an efficient framework for Data Extraction from Chart Images](https://mlsanity.com/paper/2105.02039/) (2021)
- [Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization](https://mlsanity.com/paper/2312.10610/) (2023)
- [ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition](https://mlsanity.com/paper/2308.07743/) (2023)
- [SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials](https://mlsanity.com/paper/2405.00021/) (2024)
- [Context-Aware Chart Element Detection](https://mlsanity.com/paper/2305.04151/) (2023)
- [Text2Chart: A Multi-Staged Chart Generator from Natural Language Text](https://mlsanity.com/paper/2104.04584/) (2021)
- [VizExtract: Automatic Relation Extraction from Data Visualizations](https://mlsanity.com/paper/2112.03485/) (2021)
- [A Survey and Approach to Chart Classification](https://mlsanity.com/paper/2307.04147/) (2023)
- [A Survey on Figure Classification Techniques in Scientific Documents](https://mlsanity.com/paper/2307.05694/) (2023)
- [An xAI Approach for Data-to-Text Processing with ASP](https://mlsanity.com/paper/2308.15898/) (2023)
- [Assessing Graphical Perception of Image Embedding Models using Channel Effectiveness](https://mlsanity.com/paper/2407.20845/) (2024)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)
- [ChartParser: Automatic Chart Parsing for Print-Impaired](https://mlsanity.com/paper/2211.08863/) (2022)
- [Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks](https://mlsanity.com/paper/2510.06071/) (2025)
- [Semantic Segmentation of Node and Edge Diagrams for Assistive Technology](https://mlsanity.com/paper/2606.11320/) (2026)

## Abstract (arXiv)

Images greatly help in understanding, interpreting and visualizing data. Adding textual description to images is the first and foremost principle of web accessibility. Visually impaired users using screen readers will use these textual descriptions to get better understanding of images present in digital contents. In this paper, we propose Chart-Text a novel fully automated system that creates textual description of chart images. Given a PNG image of a chart, our Chart-Text system creates a complete textual description of it. First, the system classifies the type of chart and then it detects and classifies the labels and texts in the charts. Finally, it uses specific image processing algorithms to extract relevant information from the chart images. Our proposed system achieves an accuracy of 99.72% in classifying the charts and an accuracy of 78.9% in extracting the data and creating the corresponding textual description.