A Table Detection Method for Multipage PDF Documents via Visual Seperators and Tabular Structures

Fang, Jing; Gao, Liangcai; Bai, Kun; Qiu, Ruiheng; Tao, Xin; Tang, Zhi

doi:10.1109/icdar.2011.304

Cited by 56 publications

(26 citation statements)

References 7 publications

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“…The heuristic approach locates tables by finding whitespace and line separators and filtering out regions containing paragraphs of text. It is worth noting that in [5] authors compared their evaluation results with those presented by Liu et al in [6], obtaining better precision and recall. In this competition, we were able to compare the two systems directly, and this time Liu et al obtained better results on our dataset.…”

Section: A Icst-table System Fang Et Almentioning

confidence: 86%

“…The ICST-Table system [5] was submitted by Jing Fang, Leipeng Hao, Liangcai Gao, Xin Tao and Zhi Tang from the Institute of Computer Science & Technology, Peking University, Beijing, China and is designed to recognize tables in borndigital PDFs, which are parsed using a commercial library. The heuristic approach locates tables by finding whitespace and line separators and filtering out regions containing paragraphs of text.…”

Section: A Icst-table System Fang Et Almentioning

confidence: 99%

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ICDAR 2013 Table Competition

Göbel

Hassan

Oro

et al. 2013

2013 12th International Conference on Document Analysis and Recognition

203

164

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Table understanding is a well studied problem in document analysis, and many academic and commercial approaches have been developed to recognize tables in several document formats, including plain text, scanned page images and born-digital, object-based formats such as PDF. Despite the abundance of these techniques, an objective comparison of their performance is still missing. The Table Competition held in the context of ICDAR 2013 is our first attempt at objectively evaluating these techniques against each other in a standardized way, across several input formats. The competition independently addresses three problems: (i) table location, (ii) table structure recognition, and (iii) these two tasks combined. We received results from seven academic systems, which we have also compared against four commercial products. This paper presents our findings.

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Section: A Icst-table System Fang Et Almentioning

confidence: 86%

Section: A Icst-table System Fang Et Almentioning

confidence: 99%

ICDAR 2013 Table Competition

Göbel

Hassan

Oro

et al. 2013

2013 12th International Conference on Document Analysis and Recognition

203

164

View full text Add to dashboard Cite

show abstract

“…Before the advent of deep learning, most works on document analysis for table detection were based on exploiting a priori knowledge on object properties by analyzing tokens extracted from source document files [9], [10], [11], [12], [13]. For example, [10] proposes a method for table detection in PDF documents, which uses tags of tabular separators to identify the table region.…”

Section: Related Workmentioning

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

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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.

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“…The method suggested in [3] uses statistical approach, which groups lines and finds out candidates for tables. This approach becomes template dependent, and it is difficult to use it for border less tables [7]. Identified tabular regions by tab-separated information [8].…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Learning-based Table Detection

Gurav

Nene

2020

SN COMPUT. SCI.

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CNN has given the state-of-the-art results in computer vision and natural language processing (NLP) domain problems. This has motivated researchers to use deep learning-based techniques for document layout analysis. Due to recent advances in communication and in information technology, methods of data storage, extraction and processing are rapidly evolving. In an information space, there is a large volume of digital documents (DD) already available, and more DD is created continuously. The DD can be natural images, scanned documents, mails, books, archives, etc. Processing, extraction and understanding of relevant information from these DD have prime business importance. Information in DD is present in the form of tables, text, figure, images, diagram, etc. Document images (DI) are DD which are present in the form of images. It is especially office, scanned documents. Recent progress in artificial intelligence has created a growing expectation for automation of data extraction from DD. Tables are frequently present in DI. There is a desperate need to handle tabular information present in DI; otherwise, this information will remain unused and there is high possibility that it will be lost. In this work, we present weakly supervised learning-based approach to detect and recognize the location of the table. The novelty of our work is that it did not require bounding box annotations for table detection. Application of our technique on ICDAR 2013 test data has demonstrated encouraging results.

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A Table Detection Method for Multipage PDF Documents via Visual Seperators and Tabular Structures

Cited by 56 publications

References 7 publications

ICDAR 2013 Table Competition

ICDAR 2013 Table Competition

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

Weakly Supervised Learning-based Table Detection

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