Table Detection from Document Image using Vertical Arrangement of Text Blocks

Tran, Dieu Ni; Tran, Tuan Anh; Oh, Aran; Kim, Sung Hyun; Na, In Seop

doi:10.5392/ijoc.2015.11.4.077

Cited by 60 publications

(21 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these cases, the classification loss introduced with the Inception-based classifiers significantly helped in improving the corresponding accuracy scores. To ground our work with state of the art in table detection, we compared the performance of the four different configurations of our method to those achieved by DeepDeSRT [59], Tran [34] and Hao [27] in detecting only tables on the ICDAR 2013 dataset. The comparison is reported in Table IV and highlights the importance of the different components of our method.…”

Section: Resultsmentioning

confidence: 99%

A Saliency-Based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

Kavasidis

Pino

Palazzo

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in several application domains. Document analysis is a particularly promising area for DCNNs: indeed, the number of available digital documents has reached unprecedented levels, and humans are no longer able to discover and retrieve all the information contained in these documents without the help of automation. Under this scenario, DCNNs offers a viable solution to automate the information extraction process from digital documents. Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis carried out on an extended version of ICDAR 2013 (with annotated charts as well as tables) shows that our approach yields promising results, outperforming existing models.

show abstract

Section: Resultsmentioning

confidence: 99%

A Saliency-Based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

Kavasidis

Pino

Palazzo

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…To increase the efficiency of the non-text-analysis approach, a number of improvements have been proposed such as those of [1,13,17,38]. In [13], the authors propose a Box Driven Reasoning (BDR), allows one to robustly analyze the structure of table form documents that include touching characters and broken lines.…”

Section: Non-text-analysis Methodsmentioning

confidence: 99%

“…The method of Kasar et al [17] is a learning approach that allows one to detect table regions in document images by identifying the column and row line separators as well as their properties. In [38], Tran et al propose a method to identify the table region from document images that requires the existence of table-structure lines or table-structure boundaries. The proposed method proceeds as follows.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…At present, most research on table recognition is aimed at scanned documents. Table features, such as text blocks [11,12], word spacing [13,14], column spacing [15], junctions [16], and ruling lines [17] are used to detect tables in scanned document images. All the above methods assume that the text lines and table boundaries are parallel, but they are not parallel in intelligent IoT vision device-captured images due to perspective transformation [18,19].…”

Section: Table-recognition Methodsmentioning

confidence: 99%

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Table Detection from Document Image using Vertical Arrangement of Text Blocks

Cited by 60 publications

References 10 publications

A Saliency-Based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

A Saliency-Based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

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

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

Contact Info

Product

Resources

About