Image processing based data extraction from graphical representation

Reddy, Viswanath K.; Kaushik, Chirag

doi:10.1109/cgvis.2015.7449920

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Finally, data are extracted using a sequential scanning process. Considering that the chart might have grid lines, Viswanath et al [21] proposed an image processing-based method and developed a semi-supervised system. However, the method assumes that grid lines should not be more visually distinctive than the data marks.…”

Section: Related Workmentioning

confidence: 99%

Towards an Efficient Framework for Data Extraction from Chart Images

Zhang

Yan³

et al. 2021

Document Analysis and Recognition – ICDAR 2021

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In this paper, we fill the research gap by adopting stateof-the-art computer vision techniques for the data extraction stage in a data mining system. As shown in Fig. 1, this stage contains two subtasks, namely, plot element detection and data conversion. For building a robust box detector, we comprehensively compare different deep learning-based methods and find a suitable method to detect box with high precision. For building a robust point detector, a fully convolutional network with feature fusion module is adopted, which can distinguish close points compared to traditional methods. The proposed system can effectively handle various chart data without making heuristic assumptions. For data conversion, we translate the detected element into data with semantic value. A network is proposed to measure feature similarities between legends and detected elements in the legend matching phase. Furthermore, we provide a baseline on the competition of Harvesting raw tables from Infographics. Some key factors have been found to improve the performance of each stage. Experimental results demonstrate the effectiveness of the proposed system.

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Section: Related Workmentioning

confidence: 99%

Towards an Efficient Framework for Data Extraction from Chart Images

Zhang

Yan³

et al. 2021

Document Analysis and Recognition – ICDAR 2021

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“…Image processing algorithms are used for extracting data from images. [10][11][12] But these works are limited to extracting data from line plots with a clear discrimination between data and the background. However, there is a need for preprocessing the image before data extraction.…”

Section: Some Digitization Algorithmsmentioning

confidence: 99%

A technical note on digitizing color mapped spectral power distribution images

Shailesh

2021

Color Research & Application

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Data visualization is producing images that communicate data in the form of visual objects like lines, points, bars, or colored areas. Often it is necessary to extract numerical data from these images for further analysis. There are a wide variety of data digitization tools, however, only limited formats of plot images can be digitized using them. There exists many data visualization formats spread across different domains of science. Some of these formats need tailored solutions. One such format is color mapped spectral power distribution images of light sources captured by proprietary spectrometers. In this work, a methodology is discussed to digitize spectral plots of light sources. Unlike typical digitization tools which extract data based on contrast difference, color, or morphological similarities, color mapped spectral plots contain blending colors across the x‐axis, without containing any notable morphological features. In this work, image processing is applied to extract the contour representing a spectral plot. For color mapped spectral plots, the importance of appropriate binary input images for edge detection algorithms is highlighted. There are only a few edge‐detection methods give the desired results except a few. Further, the importance of repeatedly adjusting threshold values in extracting desired contours is also discussed. The estimated and measured spectral parameters are in mutual agreement with each other thereby validating the adopted approach.

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