We introduce LEAF-QA, a comprehensive dataset of @math densely annotated figures charts, constructed from real-world open data sources, along with 2 million question-answer (QA) pairs querying the structure and semantics of these charts. LEAF-QA highlights the problem of multimodal QA, which is notably different from conventional visual QA (VQA), and has recently gained interest in the community. Furthermore, LEAF-QA is significantly more complex than previous attempts at chart QA, viz. FigureQA and DVQA, which present only limited variations in chart data. LEAF-QA being constructed from real-world sources, requires a novel architecture to enable question answering. To this end, LEAF-Net, a deep architecture involving chart element localization, question and answer encoding in terms of chart elements, and an attention network is proposed. Different experiments are conducted to demonstrate the challenges of QA on LEAF-QA. The proposed architecture, LEAF-Net also considerably advances the current state-of-the-art on FigureQA and DVQA.
There has been recent interest in analyzing figures and charts, particularly to understand the type of visualization and for data extraction from the chart images. @cite_26 describe algorithms to extract data from pie and bar charts, particularly to re-visualize them. Further, interactive methods for bar chart extraction have been studied @cite_34 @cite_22 . @cite_14 describe an object detection framework for extracting scatter plot elements. Similarly, an analysis for line plot extraction has been presented by @cite_19 . There have also been attempts at indexing of figures @cite_35 @cite_11 for search and classification. B "o @cite_15 and @cite_8 describe methods for improving text and symbol extraction from figures. @cite_7 describe a framework to restyle different kinds of visualizations, through maneuvering the data in the SVGs.
@cite_35 @cite_14 @cite_26 @cite_22 @cite_7 @cite_8 @cite_19 @cite_15 @cite_34 @cite_11
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Scholarly documents contain multiple figures representing experimental findings. These figures are generated from data which is not reported anywhere else in the paper. We propose a modular architecture for analyzing such figures. Our architecture consists of the following modules: 1. An extractor for figures and associated metadata (figure captions and mentions) from PDF documents; 2. A Search engine on the extracted figures and metadata; 3. An image processing module for automated data extraction from the figures and 4. A natural language processing module to understand the semantics of the figure. We discuss the challenges in each step, report an extractor algorithm to extract vector graphics from scholarly documents and a classification algorithm for figures. Our extractor algorithm improves the state of the art by more than 10 and the classification process is very scalable, yet achieves 85 accuracy. We also describe a semi-automatic system for data extraction from figures which is integrated with our search engine to improve user experience. Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89 of the plots in our test set. Poorly designed charts are prevalent in reports, magazines, books and on the Web. Most of these charts are only available as bitmap images; without access to the underlying data it is prohibitively difficult for viewers to create more effective visual representations. In response we present ReVision, a system that automatically redesigns visualizations to improve graphical perception. Given a bitmap image of a chart as input, ReVision applies computer vision and machine learning techniques to identify the chart type (e.g., pie chart, bar chart, scatterplot, etc.). It then extracts the graphical marks and infers the underlying data. Using a corpus of images drawn from the web, ReVision achieves image classification accuracy of 96 across ten chart categories. It also accurately extracts marks from 79 of bar charts and 62 of pie charts, and from these charts it successfully extracts data from 71 of bar charts and 64 of pie charts. ReVision then applies perceptually-based design principles to populate an interactive gallery of redesigned charts. With this interface, users can view alternative chart designs and retarget content to different visual styles. The D3 JavaScript library has become a ubiquitous tool for developing visualizations on the Web. Yet, once a D3 visualization is published online its visual style is difficult to change. We present a pair of tools for deconstructing and restyling existing D3 visualizations. Our deconstruction tool analyzes a D3 visualization to extract the data, the marks and the mappings between them. Our restyling tool lets users modify the visual attributes of the marks as well as the mappings from the data to these attributes. Together our tools allow users to easily modify D3 visualizations without examining the underlying code and we show how they can be used to deconstruct and restyle a variety of D3 visualizations. We investigate how to automatically recover visual encodings from a chart image, primarily using inferred text elements. We contribute an end-to-end pipeline which takes a bitmap image as input and returns a visual encoding specification as output. We present a text analysis pipeline which detects text elements in a chart, classifies their role e.g., chart title, x-axis label, y-axis title, etc., and recovers the text content using optical character recognition. We also train a Convolutional Neural Network for mark type classification. Using the identified text elements and graphical mark type, we can then infer the encoding specification of an input chart image. We evaluate our techniques on three chart corpora: a set of automatically labeled charts generated using Vega, charts from the Quartz news website, and charts extracted from academic papers. We demonstrate accurate automatic inference of text elements, mark types, and chart specifications across a variety of input chart types. Information graphics, such as graphs and plots, are used in technical documents to convey information to humans and to facilitate greater understanding. Usually, graphics are a key component in a technical document, as they enable the author to convey complex ideas in a simplified visual format. However, in an automatic text recognition system, which are typically used to digitize documents, the ideas conveyed in a graphical format are lost. We contend that the message or extracted information can be used to help better understand the ideas conveyed in the document. In scientific papers, line plots are the most commonly used graphic to represent experimental results in the form of correlation present between values represented on the axes. The contribution of our work is in the series of image processing algorithms that are used to automatically extract relevant information, including text and plot from graphics found in technical documents. We validate the approach by performing the experiments on a dataset of line plots obtained from scientific documents from computer science conference papers and evaluate the variation of a reconstructed curve from the original curve. Our algorithm achieves a classification accuracy of 91 across the dataset and successfully extracts the axes from 92 of line plots. Axes label extraction and line curve tracing are performed successfully in about half the line plots as well. Existing research on analyzing information graphics assume to have a perfect text detection and extraction available. However, text extraction from information graphics is far from solved. To fill this gap, we propose a novel processing pipeline for multi-oriented text extraction from infographics. The pipeline applies a combination of data mining and computer vision techniques to identify text elements, cluster them into text lines, compute their orientation, and uses a state-of-the-art open source OCR engine to perform the text recognition. We evaluate our method on 121 infographics extracted from an open access corpus of scientific publications. The results show that our approach is effective and significantly outperforms a state-of-the-art baseline. Charts are commonly used to present data in digital documents such as web pages, research papers, or presentation slides. When the underlying data is not available, it is necessary to extract the data from a chart image to utilize the data for further analysis or improve the chart for more accurate perception. In this paper, we present ChartSense, an interactive chart data extraction system. ChartSense first determines the chart type of a given chart image using a deep learning based classifier, and then extracts underlying data from the chart image using semi-automatic, interactive extraction algorithms optimized for each chart type. To evaluate chart type classification accuracy, we compared ChartSense with ReVision, a system with the state-of-the-art chart type classifier. We found that ChartSense was more accurate than ReVision. In addition, to evaluate data extraction performance, we conducted a user study, comparing ChartSense with WebPlotDigitizer, one of the most effective chart data extraction tools among publicly accessible ones. Our results showed that ChartSense was better than WebPlotDigitizer in terms of task completion time, error rate, and subjective preference.