Segmentation and validation of commercial documents logical structure

Matrakas, Miguel Diogenes; Bortolozzi, Flávio

doi:10.1109/itcc.2000.844221

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The advantage of those methods is the high detection speed as they are not containing time-consuming operations, but they cannot handle documents well with very complex layouts. Some examples of top-down algorithms are reported in [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Text localization using standard deviation analysis of structure elements and support vector machines

Zagoris

Chatzichristofis

Papamarkos

2011

EURASIP J. Adv. Signal Process.

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A text localization technique is required to successfully exploit document images such as technical articles and letters. The proposed method detects and extracts text areas from document images. Initially a connected components analysis technique detects blocks of foreground objects. Then, a descriptor that consists of a set of suitable document structure elements is extracted from the blocks. This is achieved by incorporating an algorithm called Standard Deviation Analysis of Structure Elements (SDASE) which maximizes the separability between the blocks. Another feature of the SDASE is that its length adapts according to the requirements of the application. Finally, the descriptor of each block is used as input to a trained support vector machines that classify the block as text or not. The proposed technique is also capable of adjusting to the text structure of the documents. Experimental results on benchmarking databases demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Text localization using standard deviation analysis of structure elements and support vector machines

Zagoris

Chatzichristofis

Papamarkos

2011

EURASIP J. Adv. Signal Process.

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“…To classify blocks, they use structural and geometric data, such as x-y positions, number of lines, and contextual data like the relative positions of blocks. Matrakas et al 5 propose another method based on geometric information, that uses the nearest neighbor rule technique. The use of geometric elements for labeling gives interesting results.…”

mentioning

confidence: 99%

“…RLSA (Run Length Smoothing Algorithm) is used for example in Ref. 5. This is convenient for printed document.…”

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confidence: 99%

<title>A generic method for structure recognition of handwritten mail documents</title>

2008

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This paper presents a system to extract the logical structure of handwritten mail documents. It consists in two joined tasks: the segmentation of documents into blocks and the labeling of such blocks. The main considered label classes are: addressee details, sender details, date, subject, text body, signature. This work has to face with difficulties of unconstrained handwritten documents: variable structure and writing.We propose a method based on a geometric analysis of the arrangement of elements in the document. We give a description of the document using a two-dimension grammatical formalism, which makes it possible to easily introduce knowledge on mail into a generic parser. Our grammatical parser is LL(k), which means several combinations are tried before extracting the good one. The main interest of this approach is that we can deal with low structured documents. Moreover, as the segmentation into blocks often depends on the associated classes, our method is able to retry a different segmentation until labeling succeeds.We validated this method in the context of the French national project RIMES, which proposed a contest on a large base of documents. We obtain a recognition rate of 91.7% on 1150 images.

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Development and Evaluation of Text Localization Techniques Based on Structural Texture Features and Neural Classifiers

Emmanouilidis

Batsalas

Papamarkos

2009

2009 10th International Conference on Document Analysis and Recognition

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Segmentation and validation of commercial documents logical structure

Cited by 3 publications

References 3 publications

Text localization using standard deviation analysis of structure elements and support vector machines

Text localization using standard deviation analysis of structure elements and support vector machines

<title>A generic method for structure recognition of handwritten mail documents</title>

Development and Evaluation of Text Localization Techniques Based on Structural Texture Features and Neural Classifiers

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