Abstract
Table detection in mobile captured images faces many challenges owning to the well-known low image quality. Recently, a few researches pioneer in detecting the tables in rich-text images, but few works for scorecard images which usually lack of texts but are rich in graphics, such as golf scorecard images. In this paper, a junction-relation based table detection method for mobile captured scorecard images is proposed. Firstly, the most distinguished junctions are determined via a simplified pattern matching method, then the fault detections are removed through filtering operations, finally the missed junctions are recovered utilizing the pair-wise relationships among neighboring junctions. The experimental results show that 98.47 % of the junctions from 90 test images are correctly detected, and thus proves the superiority of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kise, K.: Handbook of Document Image Processing and Recognition. Springer, London (2014)
Chen, J., Lopresti, D.: Ruling-based table analysis for noisy handwritten documents. In: Proceedings of the 4th International Workshop on Multilingual OCR. ACM (2013)
Gatos, B., Pratikakis, I., Perantonis, S.J.: An adaptive binarization technique for low quality historical documents. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 102–113. Springer, Heidelberg (2014)
Cesarini, F., Marinai, S., Sarti, L., Soda, G.: Trainable table location in document images. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 3, pp. 236–240. IEEE (2002)
Ha, J., Haralick, R.M., Phillips, I.T.: Recursive X-Y cut using bounding boxes of connected components. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 2, pp. 952–955. IEEE (1995)
Liang, J., Doermann, D., Li, H.: Camera-based analysis of text and documents: a survey. Int. J. Doc. Anal. Recogn. (IJDAR) 7(2–3), 84–104 (2005)
Mirmehdi, M.: Special issue on camera-based text and document recognition. Int. J. Doc. Anal. Recogn. (IJDAR) 7(2–3), 83 (2005)
Seo, W., Koo, H., Cho, N.: Junction-based table detection in camera-captured document images. Int. J. Doc. Anal. Recogn. (IJDAR) 18(1), 47–57 (2015)
Yuan, J.Y., Chen, H.S., Cao, H.R.: An efficient junction detection approach for mobile-captured golf scorecard images. Procedia Comput. Sci. 55, 792–801 (2015)
Acknowledgements
This work is supported by the Foundation for Distinguished Young Talents in Higher Education, the Teaching Quality and Teaching Reform Project the Science and Technology Project of Guangdong, China, with Grant Nos. 2013LYM0123, ZL2013025, and 2013B090500067 respectively. Any options, findings, and conclusions or recommendations expressed in this paper are those of the authors. Also the authors would like to thank Guangzhou Gaoyou-Box Ltd. for the support on the golf scorecards.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yuan, J., Chen, H., Cao, H., Guo, Z. (2016). Junction Based Table Detection in Mobile Captured Golf Scorecard Images. In: Wan, J., Humar, I., Zhang, D. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-319-44350-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-44350-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44349-2
Online ISBN: 978-3-319-44350-8
eBook Packages: Computer ScienceComputer Science (R0)