A methodology for evaluating algorithms for table understanding in PDF documents

Göbel, Max; Hassan, Tamir; Oro, Ermelinda; Orsi, Giorgio

doi:10.1145/2361354.2361365

Cited by 59 publications

(34 citation statements)

References 11 publications

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“…In our previous work [1], we proposed a methodology for evaluating these approaches independently from the format of the input and of the output. In particular, the problem of table understanding was split up into three tasks: 1) Many approaches to table understanding, e.g.…”

Section: Introductionmentioning

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

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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: Introductionmentioning

confidence: 99%

“…In particular, the problem of table understanding was split up into three tasks: 1) Many approaches to table understanding, e.g. [2], [3], have been designed to work on object-based documents as input and therefore cannot be evaluated using datasets consisting solely of page images.…”

Section: Introductionmentioning

confidence: 99%

ICDAR 2013 Table Competition

Göbel

Hassan

Oro

et al. 2013

2013 12th International Conference on Document Analysis and Recognition

Self Cite

203

164

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show abstract

“…There are many datasets and metrics were proposed to deal with this problem [2-4, 6, 18]. In this paper, we focus on the metric based on text regions (sub-object elements) [1,2,6] which was used to evaluating the results of competitor's algorithms in ICDAR2013 table competition.…”

Section: Resultsmentioning

confidence: 99%

“…This evaluation profile was first introduced by Silva [2] in 2011 and be used for the performance evaluation of region results (table location-LOC) in ICDAR2013 table competition [1,6]. Firstly, they compare the performance of the method by measuring of completeness and purity of each table region.…”

Section: Resultsmentioning

confidence: 99%

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A hybrid method for table detection from document image

Anh

Kim

2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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In this paper, we present a hybrid method consisting of three main stages for detecting tables in document images. Based on table structure, our system separates table into two main categories, ruling line table and nonruling line table. In the first stage, the text and non-text elements in document are classified by a heuristic filter. Then, the white space analysis is used to group the text elements into text lines, while ruling line table candidates are identified from non-text elements. In the second stage, based on the text lines, text and non-text elements, a hybrid method which consist of the alternative bottom-up and top-down approaches is implemented to find the table region candidates. In the final stage, these candidates are examined to get the table regions by analyzing text lines and spare lines. Experimental results with the document database from the ICDAR2013 table competition show that the proposed method works better than the previous ones.

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Table understanding: Problem overview

Shigarov

2022

WIREs Data Min & Knowl

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Tables are probably the most natural way to represent relational data in various media and formats. They store a large number of valuable facts that could be utilized for question answering, knowledge base population, natural language generation, and other applications. However, many tables are not accompanied by semantics for the automatic interpretation of the information they present. Table Understanding (TU) aims at recovering the missing semantics that enables the extraction of facts from tables. This problem covers a range of issues from table detection in document images to semantic table interpretation with the help of external knowledge bases. To date, the TU research has been ongoing on for 30 years. Nevertheless, there is no common point of view on the scope of TU; the terminology still needs agreement and unification. In recent years, science and technology have shown a rapidly increasing interest in TU. Nowadays, it is especially important to check the meaning of this research problem once again. This article gives a comprehensive characterization of the TU problem, including a description of its subproblems, tasks, subtasks, and applications. It also discusses the common limitations used in the existing problem statements and proposes some directions for further research that would help overcome the corresponding limitations. This article is categorized under: Algorithmic Development > Text Mining Algorithmic Development > Web Mining

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A methodology for evaluating algorithms for table understanding in PDF documents

Cited by 59 publications

References 11 publications

ICDAR 2013 Table Competition

ICDAR 2013 Table Competition

A hybrid method for table detection from document image

Table understanding: Problem overview

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