TY - GEN
T1 - A Robust Table Detection Method for Distortion in Image Acquired from Camera
AU - Nakaigawa, Toshiya
AU - Mashiyama, Yoshiki
AU - Mitsukura, Yasue
AU - Hamada, Nozomu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
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.
AB - 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.
KW - camera-based document analysis and recognition (CBDAR)
KW - image processing
KW - table detection
UR - http://www.scopus.com/inward/record.url?scp=85084050928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084050928&partnerID=8YFLogxK
U2 - 10.1109/IECON.2019.8926741
DO - 10.1109/IECON.2019.8926741
M3 - Conference contribution
AN - SCOPUS:85084050928
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 5347
EP - 5352
BT - Proceedings
PB - IEEE Computer Society
T2 - 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Y2 - 14 October 2019 through 17 October 2019
ER -