Robust table-form structure analysis based on box-driven reasoning

Hori, Osamu; Doermann, David

doi:10.1109/icdar.1995.598980

Cited by 45 publications

(25 citation statements)

References 5 publications

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“…Line segment recognition has, however, been steadily improved during the last three decades as part of table interpretation [1,2,3,4,5], form processing [6,7,8,9], and engineering drawing analysis [10,11,12,13]. Historical form analysis [14,15] became popular even as most contemporary forms migrated to the web.…”

Section: Prior Workmentioning

confidence: 99%

On Parallel Lines in Noisy Forms

Nagy

2014

Lecture Notes in Computer Science

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Abstract. Quantification of the rectilinear configuration of typeset rules (lines) opens the way to form classification and content extraction. Line detection on scanned forms is often accomplished with the Hough transform. Here it is followed by simultaneous extraction of the dominant perpendicular sets of extracted lines, which ensures rotation invariance. Translation and scale invariance are attained by using minimal horizontal and vertical sets of distance ratios ("rule gap ratios") instead of rule-edge locations. The ratios are logarithmically mapped to an alphabet so that the resulting symbol strings can be classified by. edit distance. Some probability distributions associated with these steps are derived. Analytical considerations and small-scale experiments on scanned forms suggest that this approach has potential merit for highly degraded forms.

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Section: Prior Workmentioning

confidence: 99%

On Parallel Lines in Noisy Forms

Nagy

2014

Lecture Notes in Computer Science

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“…However, their method is not able to deal with curvature or with slope bigger than 2 degrees, that frequently occurs in digitized documents. Hori and Doerman present in [5] a multi-level analysis for form recognition. We follow this idea in order to solve more generally the problem of ruling recognition.…”

Section: Introductionmentioning

confidence: 99%

Use of Perceptive Vision for Ruling Recognition in Ancient Documents

Lemaitre

Coüasnon

Camillerapp

2010

Graphics Recognition. Achievements, Challenges, and Evolution

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Abstract. Rulings are graphical primitives that are essential for document structure recognition. However in the case of ancient documents, bad printing techniques or bad conditions of conservation induce problems for their efficient recognition. Consequently, usual line segment extractors are not powerful enough to properly extract all the rulings of a heterogeneous document. In this paper, we propose a new method for ruling recognition, based on perceptive vision: we show that combining several levels of vision improves ruling recognition. Thus, it is possible to put forward hypothesis on the nature of the rulings at a given resolution, and to confirm or infirm their presence and find their exact position at higher resolutions. We propose an original strategy of cooperation between resolutions and present tools to set up a correspondence between the elements extracted at each resolution. We validate this approach on images of ancient newspaper pages (dated between 1848 and 1944). We also propose to use the extracted rulings for the structure analysis of newspaper pages. We show that using more reliable extracted rulings simplifies and improves document structure recognition.

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“…In known form processing the number of lines and the gaps among these lines can be used as a priori knowledge and stored as references in form templates. These ideas have been presented in previous work to improve detection accuracy and reduce false alarms [14,1,15,16]. But the usage of a priori knowledge in the above applications is ad hoc and lacks a systematic representation.…”

Section: Rule Line Detectionmentioning

confidence: 99%

Handwriting Identification, Matching, and Indexing in Noisy Document Images

Zheng¹

2006

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Throughout history, handwriting has been the primary means of recording information that is persevered across both time and space. With the coming of the electronic document era, we are challenged with making an enormous amount of handwritten documents available for electronic access. Though many handwritten documents contain only handwriting, now, more are mixed with printed text, noise, and background patterns. The mixture of handwriting with other components presents a great challenge for making an original document electronically accessible.Many handwritten documents come together with a special background pattern, rule lines, which are printed on the paper to guide writing. After digitization, rule lines will touch text and cause problems for further document image analysis if they are not detected and removed. In this dissertation, we present a rule line detection algorithm based on hidden Markov model (HMM) decoding, achieving both high detection accuracy and a low false alarm rate. After detection, line removal is performed by line width thresholding.Handwriting often mixes with printed text, such as signatures and annotations on a business letter. Handwriting in a printed document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content. The data set we are processing is noisy, which makes the problem more challenging. In this dissertation, we first segment the document at a suitable level, and then classify each segmented block as machine printed text, handwriting, or noise. Markov random field (MRF) based post-processing is exploited to refine the classification results.The identified handwriting may be further analyzed. In this dissertation, we propose a novel point-pattern based handwriting matching technique and apply it for handwriting synthesis and retrieval. We formulate point matching as an optimization problem trying to preserve the local neighborhood structures. After establishing the correspondence between two handwriting samples, we warp one sample toward the other using the thin plate spline (TPS) deformation model to synthesize new handwriting samples. We also apply our matching algorithm for handwriting retrieval since it is much easier to define robust features based on the matching

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Robust table-form structure analysis based on box-driven reasoning

Cited by 45 publications

References 5 publications

On Parallel Lines in Noisy Forms

On Parallel Lines in Noisy Forms

Use of Perceptive Vision for Ruling Recognition in Ancient Documents

Handwriting Identification, Matching, and Indexing in Noisy Document Images

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