Research Article
Junction Based Table Detection in Mobile Captured Golf Scorecard Images
@INPROCEEDINGS{10.1007/978-3-319-44350-8_18, author={Junying Yuan and Haishan Chen and Huiru Cao and Zhonghua Guo}, title={Junction Based Table Detection in Mobile Captured Golf Scorecard Images}, proceedings={Industrial IoT Technologies and Applications. International Conference, Industrial IoT 2016, GuangZhou, China, March 25-26, 2016, Revised Selected Papers}, proceedings_a={INDUSTRIALIOT}, year={2016}, month={9}, keywords={Mobile captured images Junction detection Table detection Pair-wise relationship Junction filtering Junction recovery}, doi={10.1007/978-3-319-44350-8_18} }- Junying Yuan
Haishan Chen
Huiru Cao
Zhonghua Guo
Year: 2016
Junction Based Table Detection in Mobile Captured Golf Scorecard Images
INDUSTRIALIOT
Springer
DOI: 10.1007/978-3-319-44350-8_18
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.