A Robust Table Detection Method for Distortion in Image Acquired from Camera

Toshiya Nakaigawa; Yoshiki Mashiyama; Yasue Mitsukura; Nozomu Hamada

doi:10.1109/IECON.2019.8926741

A Robust Table Detection Method for Distortion in Image Acquired from Camera

Toshiya Nakaigawa, Yoshiki Mashiyama, Yasue Mitsukura, Nozomu Hamada

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90°. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
Pages	5347-5352
Number of pages	6
ISBN (Electronic)	9781728148786
DOIs	https://doi.org/10.1109/IECON.2019.8926741
Publication status	Published - 2019 Oct
Event	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal Duration: 2019 Oct 14 → 2019 Oct 17

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2019-October

Conference

Conference	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/Territory	Portugal
City	Lisbon
Period	19/10/14 → 19/10/17

Keywords

camera-based document analysis and recognition (CBDAR)
image processing
table detection

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IECON.2019.8926741

Cite this

Nakaigawa, T., Mashiyama, Y., Mitsukura, Y., & Hamada, N. (2019). A Robust Table Detection Method for Distortion in Image Acquired from Camera. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 5347-5352). [8926741] (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IECON.2019.8926741

A Robust Table Detection Method for Distortion in Image Acquired from Camera. / Nakaigawa, Toshiya; Mashiyama, Yoshiki; Mitsukura, Yasue et al.

Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2019. p. 5347-5352 8926741 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nakaigawa, T, Mashiyama, Y, Mitsukura, Y & Hamada, N 2019, A Robust Table Detection Method for Distortion in Image Acquired from Camera. in Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society., 8926741, IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, IEEE Computer Society, pp. 5347-5352, 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019, Lisbon, Portugal, 19/10/14. https://doi.org/10.1109/IECON.2019.8926741

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title = "A Robust Table Detection Method for Distortion in Image Acquired from Camera",

abstract = "In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90°. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.",

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author = "Toshiya Nakaigawa and Yoshiki Mashiyama and Yasue Mitsukura and Nozomu Hamada",

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N2 - In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90°. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.

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A Robust Table Detection Method for Distortion in Image Acquired from Camera

Abstract

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Keywords

ASJC Scopus subject areas

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