Junction-based table detection in camera-captured document images

Seo, Wonkyo; Koo, Hyung Il; Cho, Nam Ik

doi:10.1007/s10032-014-0226-7

Cited by 29 publications

(14 citation statements)

References 27 publications

(50 reference statements)

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“…CamCap is the last dataset which we have included in this survey consists of the camera-captured images. This dataset is proposed by Seo et al [21]. It contains only 85 images (38 tables on curved surfaces having 1295 cells and 47 tables on the planar surfaces consisting of 1162 cells).…”

Section: O Camcapmentioning

confidence: 99%

“…To the best of our knowledge, there is no notable work that has employed deep learning for table recognition in camera-captured images. However, in the literature, one heuristic based approach [21] exists that works with camera-captured document images. The scope of this survey is to assess the deep learning-based approaches that have performed table recognition on the scanned document images.…”

Section: Introductionmentioning

confidence: 99%

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Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks

et al. 2021

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The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recognition are pivotal problems in the domain of table understanding. However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables. Therefore, it is an active area of research in document image analysis. Recent advances in the computing capabilities of graphical processing units have enabled the deep neural networks to outperform traditional state-of-the-art machine learning methods. Table understanding has substantially benefited from the recent breakthroughs in deep neural networks. However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition. This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks. This work provided a thorough understanding of the current state-of-the-art and related challenges of table understanding in document images. Furthermore, the leading datasets and their intricacies have been elaborated along with the quantitative results. Moreover, a brief overview is given regarding the promising directions that can serve as a guide to further improve table analysis in document images.

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Section: O Camcapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks

et al. 2021

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“…Reference [33] presents a method for locating tables and their cells in cameracaptured document images. In order to deal with this problem in the presence of geometric and photometric distortions, the authors develop new junction-detection and labeling methods.…”

Section: Non-text-analysis Methodsmentioning

confidence: 99%

Extraction of Scores and Average From Algerian High-School Degree Transcripts

Kefali¹,

Drabsia²,

Sari³

et al. 2020

csci

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A system for extracting scores and average from Algerian High School Degree Transcripts is proposed. The system extracts the scores and the average based on the localization of the tables gathering this information and it consists of several stages. After preprocessing, the system locates the tables using ruling-lines information as well as other text information. Therefore, the adopted localization approach can work even in the absence of certain ruling-lines or the erasure and discontinuity of lines. After that, the localized tables are segmented into columns and the columns into information cells. Finally, cells labeling is done based on the prior knowledge of the tables structure allowing to identify the scores and the average. Experiments have been conducted on a local dataset in order to evaluate the performances of our system and compare it with three public systems at three levels, and the obtained results show the effectiveness of our system.

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“…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%

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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Junction-based table detection in camera-captured document images

Cited by 29 publications

References 27 publications

Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks

Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks

Extraction of Scores and Average From Algerian High-School Degree Transcripts

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

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