Junction Based Table Detection in Mobile Captured Golf Scorecard Images

Yuan, Junying; Chen, Haishan; Cao, Hongchao; Guo, Zhu Ming

doi:10.1007/978-3-319-44350-8_18

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…As shown in Table 2, the accuracy of our method is higher than Yuan's [22] and Zheng's [23], no matter in what kind of perspective transformation. Among them, our method works best in the case of rotation.…”

Section: Perspective Transformation Analysismentioning

confidence: 85%

“…There is a small amount of research on table recognition in intelligent IoT vision device-captured images, all of which is based on the junction detection method [20][21][22]. These methods match the corners of table cells to labels which include connectivity information between junctions.…”

Section: Table-recognition Methodsmentioning

confidence: 99%

“…405 tables with 15,597 table cells in total are taken from the dataset, in which 11,250 table cells belong to entry fields, and 4347 belong to fixed fields. Based on these data, and the proposed evaluation metric, our method of table recognition is compared to Yuan's method [22] and Zheng's method [23] to analyze the recognition results.…”

Section: Perspective Transformation Analysismentioning

confidence: 99%

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Table Recognition for Sensitive Data Perception in an IoT Vision Environment

et al. 2019

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Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.

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Section: Perspective Transformation Analysismentioning

confidence: 85%

Section: Table-recognition Methodsmentioning

confidence: 99%

Section: Perspective Transformation Analysismentioning

confidence: 99%

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