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17th International Conference on Document Analysis and Recognition (ICDAR)

作者： Lal, Jay Mitkari, Aditya Bhosale, Mahesh Doermann, David Univ Buffalo Dept Comp Sci & Engn Artificial Intelligence Innovat Lab A2IL Buffalo NY 14068 USA

ISBN: (纸本)9783031417337;9783031417344

data extraction from line-chart images is an essential component of the automated document understanding process, as line charts are a ubiquitous data visualization format. However, the amount of visual and structural variations in multi-line graphs makes them particularly challenging for automated parsing. Existing works, however, are not robust to all these variations, either taking an all-chart unified approach or relying on auxiliary information such as legends for line data extraction. In this work, we propose LineFormer, a robust approach to line data extraction using instance segmentation. We achieve state-of-the-art performance on several benchmark synthetic and real chart datasets. Our implementation is available at https://***/TheJaeLal/LineFormer.

关键词： Line charts chart data extraction chart OCR Instance Segmentation

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data extraction of Circular-Shaped and Grid-like chart Images

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JOURNAL OF IMAGING 2022年第5期8卷 136-136页

作者： Bajic, Filip Job, Josip Univ Zagreb Univ Comp Ctr Zagreb 10000 Croatia Fac Elect Engn Comp Sci & Informat Technol Osijek Osijek 31000 Croatia

chart data extraction is a crucial research field in recovering information from chart images. With the recent rise in image processing and computer vision algorithms, researchers presented various approaches to tackle this problem. Nevertheless, most of them use different datasets, often not publicly available to the research community. Therefore, the main focus of this research was to create a chart data extraction algorithm for circular-shaped and grid-like chart types, which will accelerate research in this field and allow uniform result comparison. A large-scale dataset is provided containing 120,000 chart images organized into 20 categories, with corresponding ground truth for each image. Through the undertaken extensive research and to the best of our knowledge, no other author reports the chart data extraction of the sunburst diagrams, heatmaps, and waffle charts. In this research, a new, fully automatic low-level algorithm is also presented that uses a raster image as input and generates an object-oriented structure of the chart of that image. The main novelty of the proposed approach is in chart processing on binary images instead of commonly used pixel counting techniques. The experiments were performed with a synthetic dataset and with real-world chart images. The obtained results demonstrate two things: First, a low-level bottom-up approach can be shared among different chart types. Second, the proposed algorithm achieves superior results on a synthetic dataset. The achieved average data extraction accuracy on the synthetic dataset can be considered state-of-the-art within multiple error rate groups.

关键词： chart data extraction chart image processing data visualization image processing and computer vision

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chartReformer: Natural Language-Driven chart Image Editing 18th

ChartReformer: Natural Language-Driven Chart Image Editing

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18th International Conference on Document Analysis and Recognition (ICDAR)

作者： Yan, Pengyu Bhosale, Mahesh Lal, Jay Adhikari, Bikhyat Doermann, David Univ Buffalo Buffalo NY 14228 USA

ISBN: (纸本)9783031705328;9783031705335

chart visualizations are essential for data interpretation and communication;however, most charts are only accessible in image format and lack the corresponding data tables and supplementary information, making it difficult to alter their appearance for different scenarios of application. To eliminate the need for original underlying data and information to perform chart editing, we propose chartReformer, a natural language-driven chart image editing solution that directly edits the charts from the input images with the given instruction prompts. Instead of predicting the plotting code, the key in this method is that we allow the model to comprehend the chart and reason over the prompt to generate the corresponding underlying data table and visual attributes for new charts, enabling a precise and stable editing result. To generalize chartReformer, we define and standardize the chart editing category and generate the chartCraft dataset, covering style, layout, format, and data-centric edits. The experiments show promising results for the natural language-driven chart image editing. Our datasets and model are available at: https://***/pengyu965/chartReformer.

关键词： chart Editing chart Appearance Editing chart data extraction chart Understanding Visual Language Model

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PiExtract: An End-to-End data extraction Pipeline for Pie-charts 27th

PiExtract: An End-to-End Data Extraction Pipeline for Pie-C...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Kanroo, Muhammad Suhaib Kawoosa, Hadia Showkat Dhar, Joy Goyal, Puneet Indian Institute of Technology Ropar Punjab Rupnagar140001 India NIMS Institute of Engineering and Technology NIMS University Rajasthan Jaipur303121 India

ISBN: (纸本)9783031781216

charts are important non-textual elements present in documents, providing a visual representation of numerical data. Among different representations, Pie-charts are commonly employed in digital documents due to their perceptual advantages for displaying numerical data and inter-relationship information. chart data extraction is a multi-stage pipeline, with each stage playing a crucial role in obtaining the raw data correctly. Prior work mostly focuses on improving the performance of one or a combination of a few sub-stages. In this work, we propose a novel end-to-end data extraction algorithm, PieExtract, to extract data from pie-charts. This proposed algorithm designs a novel Robust Fusion Attention Network (RobFA-Net) approach for chart classification tasks. This network introduces a robust fusion attention strategy to learn significant discriminative global and local information, thereby enhancing the learning model performance. In addition, our novel rule-based sector data extraction method further enhances its performance in extracting data from pie-charts. Extensive experimentation is conducted on three datasets, specifically Revision, Chagas, and FigureQA, focusing on chart classification and the FigureQA dataset for data extraction from pie-charts. Our findings demonstrate that the proposed pipeline outperforms compared to previous works, showcasing superior performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attention chart data extraction Classification computer vision Pie-chart

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LYLAA: A Lightweight YOLO based Legend and Axis Analysis method for chart-Infographics 23

LYLAA: A Lightweight YOLO based Legend and Axis Analysis met...

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23rd ACM Symposium on Document Engineering (DocEng)

作者： Kawoosa, Hadia Showkat Kanroo, Muhammad Suhaib Goyal, Puneet Indian Inst Technol Ropar Rupnagar India

ISBN: (纸本)9798400700279

chart data extraction (CDE) is a complex task in document analysis that involves extracting data from charts to facilitate accessibility for various applications, such as document mining, medical diagnosis, and accessibility for the visually impaired. CDE is challenging due to the intricate structure and specific semantics of charts, which include elements such as title, axis, legend, and plot elements. The existing solutions for CDE have not yet satisfactorily addressed these issues. In this paper, we focus on two critical subtasks in CDE, Legend Analysis and Axis Analysis, and present a lightweight YOLO-based method for detection and domain-specific heuristic algorithms (Axis Matching and Legend Matching), for matching. We evaluate the efficacy of our proposed method, LYLAA, on a real-world dataset, the ICPR2022 UB PMC dataset, and observe promising results compared to the competing teams in the ICPR2022 chart-Infographics competition. Our findings showcase the potential of our proposed method in the CDE process.

关键词： Object Detection chart data extraction chart Infographics

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Context-Aware chart Element Detection 17th

Context-Aware Chart Element Detection

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17th International Conference on Document Analysis and Recognition (ICDAR)

作者： Yan, Pengyu Ahmed, Saleem Doermann, David Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14068 USA

ISBN: (纸本)9783031416750;9783031416767

As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory. In contrast to object detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, we propose a novel method CACHED, which stands for Context-Aware chart Element Detection, by integrating a local-global context fusion module consisting of visual context enhancement and positional context encoding with the Cascade R-CNN framework. To improve the generalization of our method for broader applicability, we refine the existing chart element categorization and standardized 18 classes for chart basic elements, excluding plot elements. Our CACHED method, with the updated category of chart elements, achieves state-of-the-art performance in our experiments, underscoring the importance of context in chart element detection. Extending our method to the bar plot detection task, we obtain the best result on the PMC test dataset. Our code and model are available at https://***/pengyu965/chartDete.

关键词： chart Detection chart data extraction chart Understanding Document Analysis

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ACCirO: A System for Analyzing and Digitizing Images of charts with Circular Objects 22nd

ACCirO: A System for Analyzing and Digitizing Images of Char...

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22nd Annual International Conference on Computational Science (ICCS)

作者： Daggubati, Siri Chandana Sreevalsan-Nair, Jaya Int Inst Informat Technol Bangalore IIITB Graph Visualizat Comp Lab GVCL 26-C Bengaluru 560100 Karnataka India

ISBN: (纸本)9783031087578;9783031087561

Automated interpretation of digital images of charts in documents and the internet helps to improve the accessibility of visual representation of data. One of the approaches for automation involves extraction of graphical objects in the charts, e.g., pie segments, scatter points, etc., along with its semantics encoded in the textual content of the chart. The scatter plots and pie charts are amongst the widely used infographics for data analysis, and commonly have circle objects. Here, we propose a chart interpretation system, ACCirO (Analyzer of charts with Circular Objects), that exploits the color and geometry of circular objects in scatter plots, its variants, and pie charts to extract the data from its images. ACCirO uses deep learning-based chart-type classification and OCR for text recognition to add semantics, and templatized sentence generation from the extracted data table for chart summarization. We show that image processing and deep learning approaches in ACCirO have improved the accuracy compared to the state-of-the-art.

关键词： Image processing Scatter plots Pie charts Dot plots Bubble plots Circle geometry Circle Hough Transform (CHT) Spectral clustering chart data extraction Text recognition

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chart Mining: A Survey of Methods for Automated chart Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第11期43卷 3799-3819页

作者： Davila, Kenny Setlur, Srirangaraj Doermann, David Kota, Bhargava Urala Govindaraju, Venu Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline, such as (i) automated extraction of charts from documents;(ii) processing of multi-panel charts;(iii) automatic image classifiers to collect chart images at scale;(iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes;(v) applications of chart mining;and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.

关键词： data mining Image segmentation Portable document format data visualization Measurement Layout chart survey chart extraction multi-panel chart segmentation chart image classification chart understanding chart data extraction chart datasets

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