A simple and effective table detection system from document images

Mandal, Sekhar; Chowdhury, Shyama Prosad; Das, Amit Kumar; Chanda, Bhabatosh

doi:10.1007/s10032-005-0006-5

Cited by 58 publications

(32 citation statements)

References 12 publications

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“…Hu et al [9] developed a table detection algorithm in single column document images, where they formulated the table detection problem as an optimization task by devising table quality measures. The approach proposed by Mandal et al [16] was based on the observation that the gaps between the table columns are larger than the word gaps. Based on this observation, they were able to select the text-lines that possibly belong to table regions.…”

Section: Table Detection In Scanned Documentsmentioning

confidence: 99%

“…However, the conventional methods were developed for the images from flatbed scanners, where the table boundaries are assumed to be straight and parallel [1,5,6,[9][10][11]16,19,22,26,31]. Hence, these methods cannot be used for the camera-captured documents where this assumption fails due to the geometric distortions caused by projective transforms and curved document surfaces.…”

Section: Introductionmentioning

confidence: 99%

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Junction-based table detection in camera-captured document images

Seo

Koo

Cho

2014

IJDAR

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In this paper, we present a method that locates tables and their cells in camera-captured document images. In order to deal with this problem in the presence of geometric and photometric distortions, we develop new junction detection and labeling methods, where junction detection means to find candidates for the corners of cells, and junction labeling is to infer their connectivity. We consider junctions as the intersections of curves, and so we first develop a multiple curve detection algorithm. After the junction detection, we encode the connectivity information (including false detection) between the junctions into 12 labels, and design a cost function reflecting pairwise relationships as well as local observations. The cost function is minimized via the belief propagation algorithm, and we can locate tables and their cells from the inferred labels. Also, in order to handle multiple tables in a single page, we propose a table area detection method. Our method is based on the well-known recursive X-Y cut, however, we modify the method so that we can also deal with curved seams caused by the geometric distortions. For the evaluation of our method, we build a data set that includes a variety of camera-captured table images and make the set publicly available. Experimental results on the set show that our method successfully locates tables and their cells in camera-captured images.

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Section: Table Detection In Scanned Documentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Junction-based table detection in camera-captured document images

Seo

Koo

Cho

2014

IJDAR

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“…In this paper, since we are only focusing on table spotting, we use standard measures for document image segmentation focusing on the table regions. Hence in accordance with [13,14,16,20] we use several measures for quantitatively evaluating different aspects of our table spotting algorithm.…”

Section: Performance Measuresmentioning

confidence: 99%

Table detection in heterogeneous documents

Shafait

Smith

2010

Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

101

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“…); and the third the structural model (mimicking database-type queries). Performance measures from the information retrieval domain such as Recall, Precision [19] and combined F-measure have also been used by several authors for evaluating the performance of their table recognition algorithm [17,20]. Silva at al.…”

Section: Introductionmentioning

confidence: 99%

An open approach towards the benchmarking of table structure recognition systems

Shahab

Shafait

Kieninger

et al. 2010

Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

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Table spotting and structural analysis are just a small fraction of tasks relevant when speaking of table analysis. Today, quite a large number of different approaches facing these tasks have been described in literature or are available as part of commercial OCR systems that claim to deal with tables on the scanned documents and to treat them accordingly.However, the problem of detecting tables is not yet solved at all. Different approaches have different strengths and weak points. Some fail in certain situations or layouts where others perform better. How shall one know, which approach or system is the best for his specific job? The answer to this question raises the demand for an objective comparison of different approaches which address the same task of spotting tables and recognizing their structure. This paper describes our approach towards establishing a complete and publicly available, hence open environment for the benchmarking of table spotting and structural analysis. We provide free access to the ground truthing tool and evaluation mechanism described in this paper, describe the ideas behind and we also provide ground truth for the 547 documents of the UNLV and UW-3 datasets that contain tables.In addition, we applied the quality measures to the results that were generated by the T-Recs system which we developed some years ago and which we started to further advance since a few months.

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A simple and effective table detection system from document images

Cited by 58 publications

References 12 publications

Junction-based table detection in camera-captured document images

Junction-based table detection in camera-captured document images

Table detection in heterogeneous documents

An open approach towards the benchmarking of table structure recognition systems

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