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An automatic histogram detection and information extraction from document images

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Abstract

Histogram is an important data chart that is commonly present in scientific documents. In this paper, an automatic histogram detection and information extraction methodology, based on Hough line detector and Morphological operator, is proposed. The proffered system is comprised of three steps: pre-processing, axis detection, and chart pattern extraction. In the pre-processing step, the RGB image pattern of a histogram is converted into a binary image. Next, in the axis detection step, horizontal axis, vertical axis and title of the histogram are extracted. In this step Hough line detector methodology was applied to detect horizontal and vertical lines in the image patterns. From the set of identified vertical lines, both the endpoints of a line, having the same minimum values of x co-ordinate was considered as a vertical axis. Similarly, from the set of identified horizontal lines, the two endpoints of a line having the same maximum values of y co-ordinate were considered as a horizontal axis. With respect to the dimensions of the horizontal axis and vertical axis, a rectangular region containing horizontal axis values and label, vertical axis values and label and title are extracted. In the final chart pattern extraction step, using morphological operations, the frequency of data present in the histogram was identified. Verification and validation tests of the propounded system yielded promising results, indicative of efficient approach for extraction of histogram information.

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Authors and Affiliations

  1. Dept of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    P. H. Anagha & A. Baskar

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  1. P. H. Anagha
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  2. A. Baskar
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Correspondence to A. Baskar.

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Anagha, P.H., Baskar, A. An automatic histogram detection and information extraction from document images. Int J Speech Technol 24, 77–85 (2021). https://doi.org/10.1007/s10772-020-09756-1

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  • DOI: https://doi.org/10.1007/s10772-020-09756-1

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