ChartParser: Automatic Chart Parsing for Print-Impaired
This paper introduces a new tool called ChartParser that helps blind and low-vision individuals understand charts in scientific papers by converting them into easy-to-read tables.
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- 1 And finally, we demonstrate the viability of our approach by applying our pipeline to a real-world dataset of research papers from different sources.
- 2 Chart understanding in scientific literature has recently gained much traction and there have been several attempts to classify charts using heuristics and expert rules.
- 3 Similarly, machine learning has also been used recently to detect chart components (e.g., bar or legend) .
- 4 Also, a deep learning object detection model is trained in to identify sub-figures in compound figures.
Introduction
Academic research is advancing at an incredible pace, with thousands of scientific documents published monthly (arXiv Monthly Stats). These documents often use figures\/charts as a medium for data representation and interpretation.
However, the blind, low-vision and other print-disabled (BLV) individuals are often deprived of insights and understanding offered by these figures.
Although these are converted into non-visual, screen-reader friendly representations such as alt-text, data table, etc., there is a lot of reliance on volunteers for this conversion, making it an extremely timeconsuming process.
Methodology
Given the remarkable progress in analyzing natural scene images observed in recent years, it is generally assumed that analyzing scientific figures is a trivial task. The model is based on the ResNet50 feature pyramid network (FPN) base config and is trained on the Pub-LayNet dataset for document layout analysis.
Study Design
We also realize that the axes, legend, and data extraction modules are currently modeled and trained independently in our figure analysis approach.
Results & Findings
Potential applications of our system include helping authors provide meaningful captions to their figures in papers, improving search and retrieval of relevant information in the academic domain, generating summaries from charts, building query-answering systems, developing interfaces that can provide simple and convenient access to complex information, making charts accessi-ble for BLV individuals, and helping academic committees and publishers identify plagiarized articles. However, understanding charts\/infographics present a plethora of complex challenges.
- Potential applications of our system include helping authors provide meaningful captions to their figures in papers, improving search and retrieval of relevant information in the academic.
- However, understanding charts\/infographics present a plethora of complex challenges.
- Firstly, a high level of accuracy is expected while parsing the figure plot data, as even a small mistake in analyzing chart data can lead to.
- Even though the color is an essential cue for differentiating the plot data, it may only sometimes be present because many figures frequently reuse similar colors.
- Also, figure parsing presents an additional challenge because there is only one exemplar (the legend symbol) available for model learning, in contrast to natural image recognition.
In our future work, we will extend our pipeline to other types of charts as well including line charts, scatter plots, etc. which have an L-shaped axis, similar to bar charts and also, follow a similar algorithm for extraction of.
And finally, we demonstrate the viability of our approach by applying our pipeline to a real-world dataset of research papers from different sources.
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Practical Applications
We combine the bounding boxes with distances under 10px into a single legend name because the legend names might have multiple words. Since, these values could change for several reasons (such as image compression, scanning, etc.), we start a new group with a random pixel and gradually add pixels whose R, G, and B values are no higher than 5 compared to the average of all the pixels in the.
Also, when the x-axis is at the top of the graphic, x-axis detection may fail.
Figure Extraction
This section describes the use of a pre-trained image segmentation model based on Mask R-CNN to segment figures from research papers into various categories.
Frequently Asked Questions
Hence, our goal in this paper is to design a fully automated pipeline to extract useful information from charts, specifically bar charts, and convert them into accessible data tables. It is also challenging to extract information from charts amidst heavy clutter and.
Given the remarkable progress in analyzing natural scene images observed in recent years, it is generally assumed that analyzing scientific figures is a trivial task. The model is based on the ResNet50 feature pyramid network (FPN) base config and is trained on.
And finally, we demonstrate the viability of our approach by applying our pipeline to a real-world dataset of research papers from different sources. Chart understanding in scientific literature has recently gained much traction and there have been several attempts to classify charts.
We combine the bounding boxes with distances under 10px into a single legend name because the legend names might have multiple words. Since, these values could change for several reasons (such as image compression, scanning, etc.), we start a new group with.
In our future work, we will extend our pipeline to other types of charts as well including line charts, scatter plots, etc. which have an L-shaped axis, similar to bar charts and also, follow a similar algorithm for extraction of chart elements.
This paper introduces a new tool called ChartParser that helps blind and low-vision individuals understand charts in scientific papers by converting them into easy-to-read tables.