Location of title and author regions in document images based on the Delaunay triangulation

Xiao, Yi; Yan, Hong

doi:10.1016/j.imavis.2003.11.002

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…However, the Voronoi algorithm is computationally quite expensive when applied to these digital documents. To address this problem, Delaunay triangulation was employed to solve text line segmentation [21] and extracting author and title regions [55]. Journet et al [20] further used a spatial autocorrelation approach to highlight some periodicities and texture orientation for segmenting graphic elements in a page.…”

Section: Traditional Document Layout Segmentationmentioning

confidence: 99%

Beyond document object detection: instance-level segmentation of complex layouts

Biswas

Riba

Lladós

et al. 2021

IJDAR

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Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art.

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Section: Traditional Document Layout Segmentationmentioning

confidence: 99%

Beyond document object detection: instance-level segmentation of complex layouts

Biswas

Riba

Lladós

et al. 2021

IJDAR

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“…In [14], the logical structure of a document image with irregular layout -like the location of title and author regions -is determined by the way of a Delaunay triangulation-based method. This method is very robust and can also establish the major threshold values, such as the line space and the inter-character space, automatically, from a document image itself.…”

Section: B Document Analysismentioning

confidence: 99%

Reasoning with spatial relations over high-content images

Loménie

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Spatial relation and configuration modeling issues are gaining momentum in image analysis and pattern recognition fields in the perspective of mining high-content images or large scale image databases in a more expressive way than purely statistically. Continuing our previous efforts whereby we developed specific efficient morphological tools performing on mesh representation like Delaunay triangulations, we propose to formalize spatial relation modeling techniques dedicated to unorganized point sets. We provide an original mesh lattice framework more convenient for structural representations of large amount of image data by the means of interest points sets and their morphological analysis. The set of designed numerical operators is based on a specific dilation operator making it possible the representation of concepts like "between" or "left of" over sparse representations such as graphs. Then, for the sake of illustration and discussion, we apply these new tools to high-level queries in microscopic histo-pathological images and structural analysis of macroscopic images.

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“…Bertrand Coüasnon et al essentially used the same idea when they built a platform for provinding access by content to handwritten documents such as old military registers [25]. Other examples include detecting low contrast strings in complex tables [26], locating the title and author regions in document images [27], or robust detection of text in very noisy document images [28].…”

Section: Example 2: Open Context Bootstrapping Without Full Recognitmentioning

confidence: 99%

Pattern Recognition Methods for Querying and Browsing Technical Documentation

Tombre

2008

Lecture Notes in Computer Science

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To cite this version:Karl Tombre, Bart Lamiroy. Abstract. Graphics recognition deals with the specific pattern recognition problems found in graphics-rich documents, typical technical documentation of all kinds. In this paper, we propose a short journey through 20 years of involvement and contributions within this scientific community, and explore more precisely a few interesting issues found when the problem is to browse, query and navigate in a large and complex set of technical documents.

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Location of title and author regions in document images based on the Delaunay triangulation

Cited by 9 publications

References 23 publications

Beyond document object detection: instance-level segmentation of complex layouts

Beyond document object detection: instance-level segmentation of complex layouts

Reasoning with spatial relations over high-content images

Pattern Recognition Methods for Querying and Browsing Technical Documentation

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