Text/Graphics Separation Revisited

Tombre, Karl; Tabbone, Salvatore; Pélissier, Loïc; Lamiroy, Bart; Dosch, Philippe

doi:10.1007/3-540-45869-7_24

Cited by 112 publications

(88 citation statements)

References 16 publications

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“…R (%) P (%) (Neumann and Matas, 2012) 12 Because comics are really specific documents, real-scene text detection (Neumann and Matas, 2012) detect very few text lines. The best results we reached with a method from literature is a text/graphic separation method design for documents (Tombre et al, 2002) based on our adaptive segmentation (MCCT).…”

Section: Segmentmentioning

confidence: 97%

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Automatic Text Localisation in Scanned Comic Books

Rigaud¹,

Karatzas²,

Weijer³

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, an essential step towards a fully automatic comic book understanding. We focus on speech text as it is semantically important and represents the majority of the text present in comics. The approach is compared with existing methods of text localization found in the literature and results are presented.

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Section: Segmentmentioning

confidence: 97%

“…In these results we compare our results to other text localization methods, which were either designed for unstructured documents (Tombre et al, 2002), complex backgrounds (Neumann and Matas, 2012).…”

Section: Text Localization In Unstructured Documentsmentioning

confidence: 99%

Automatic Text Localisation in Scanned Comic Books

Rigaud¹,

Karatzas²,

Weijer³

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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“…Following the convention of text/graphics separation in document analysis (Tombre et al 2002), we distinguish two types of primitive elements as textual and graphic. Both types of elements are further recognised at two sub-levels, namely grapheme and morpheme.…”

Section: Representation Primitivesmentioning

confidence: 99%

Ontologies and representation spaces for sketch map interpretation

Wang

Worboys

2017

International Journal of Geographical Information Science

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In this paper, we present a systematic approach to sketch map interpretation. The method decomposes the elements of a sketch map into a hierarchy of categories, from the material sketch map level to the non-material representational sketch map level, and then interprets the sketch map using the five formal representation spaces that we develop. These spaces (set, graph, metric and Euclidean) provide a tiered formal representation based on standard mathematical structures. We take the view that a sketch map bears information about the physical world and systematises this using extensions of existing formal ontologies. The motivation for this work is the partially automatic extraction and integration of information from sketch maps. We propose a set of ontologies and methods as a first step in the direction of a formalisation of partially automatic extraction and integration of sketch map content. We also see this work as a contribution to spatial cognition, where researchers externalise spatial knowledge using sketch mapping. The paper concludes by working through an example that demonstrates the sketch map interpretation at different levels using the underlying method.

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“…Many of the suggested algorithms concern specific type of document form [4][5], while others can fail in the presence of broken or skewed lines or in the case that text and ruling lines are extremely overlapped [6]. In the last case, a decision has to be made if a black pixel belongs to a line or a character, in order to turn it off or keep it on, respectively.…”

Section: Introductionmentioning

confidence: 99%

An image processing self-training system for ruling line removal algorithms

Prokopiou

Kavallieratou

Stamatatos

2013

2013 18th International Conference on Digital Signal Processing (DSP)

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Abstract-Ruling line removal is an important pre-processing step in document image processing. Several algorithms have been proposed for this task. However, it is important to be able to take full advantage of the existing algorithms by adapting them to the specific properties of a document image collection. In this paper, a system is presented, appropriate for fine-tuning the parameters of ruling line removal algorithms or appropriately adapt them to a specific document image collection, in order to improve the results. The application of our method to an existed line removal algorithms is presented.

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Text/Graphics Separation Revisited

Cited by 112 publications

References 16 publications

Automatic Text Localisation in Scanned Comic Books

Automatic Text Localisation in Scanned Comic Books

Ontologies and representation spaces for sketch map interpretation

An image processing self-training system for ruling line removal algorithms

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