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Pie Chart

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...

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Updated: Sep 21, 2025

Preparation and Morphological Analysis of Chick Cranial Neural Crest Cell Cultures

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Data Extraction of Circular-Shaped and Grid-like Chart Images.

Filip Bajić¹, Josip Job²

¹University Computing Centre, University of Zagreb, 10000 Zagreb, Croatia.

Journal of Imaging

|May 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel chart data extraction algorithm and a large dataset to standardize research. The new method processes binary images, achieving state-of-the-art accuracy on synthetic data for various chart types.

Keywords:

chart data extraction chart image processing data visualization image processing and computer vision

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Area of Science:

Computer Vision
Image Processing
Data Science

Background:

Chart data extraction is vital for information recovery from visual data.
Existing methods often lack standardized datasets, hindering result comparison.
Publicly unavailable datasets limit research reproducibility and advancement.

Purpose of the Study:

Develop a chart data extraction algorithm for circular and grid-like charts.
Create a large-scale, publicly available dataset to facilitate research.
Enable uniform comparison of results in chart data extraction.

Main Methods:

Developed a novel, fully automatic low-level algorithm for chart data extraction.
Utilized binary image processing instead of traditional pixel counting techniques.
Created a large dataset of 120,000 chart images across 20 categories with ground truth.

Main Results:

The proposed algorithm demonstrates effectiveness across diverse chart types.
Achieved superior performance on a synthetic dataset, indicating state-of-the-art accuracy.
Successfully extracted data from novel chart types like sunburst diagrams, heatmaps, and waffle charts.

Conclusions:

A unified low-level approach is feasible for various chart types.
The developed algorithm offers high accuracy and advances chart data extraction.
The new dataset and algorithm will accelerate research and enable standardized comparisons.