System for an intelligent office document analysis, recognition and description

Chauvet, Philippe; López-Krahe, Jaime; Taflin, Erik; Maître, H.

doi:10.1016/0165-1684(93)90041-8

Cited by 23 publications

(8 citation statements)

References 8 publications

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“…Top-down approaches split the document into blocks, columns paragraphs, text lines, or even words. The most common top-down techniques are run-length smoothing [2], [3] and projection profiles [4]. These methods are not suitable for skewed texts because top-down methods are restricted only to rectangular blocks.…”

Section: Introductionmentioning

confidence: 99%

Document image segmentation using fuzzy classifier and the dual-tree DWT

Saeedi

Safabakhsh

Mozaffari

2009

2009 14th International CSI Computer Conference

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In this paper, we propose a new method for textual areas extraction of an image using a fuzzy classifier and dual-tree discrete wavelet transform. We have extended our text extraction scheme for classification of document images into text, background, and picture components. Three class fuzzy classifiers and a morphological post-processing operation is used for this purpose. The proposed method shows better results compared to the previous wavelet-based document image segmentation algorithms.

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Section: Introductionmentioning

confidence: 99%

Document image segmentation using fuzzy classifier and the dual-tree DWT

Saeedi

Safabakhsh

Mozaffari

2009

2009 14th International CSI Computer Conference

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“…Approaches used to tackle this problem can be divided into: top-down and bottomup. The most common top-down techniques are run-length smoothing [20]- [21] and projection profiles [22]- [25]. Topdown approaches are splitting image into regions which are later identified and further spitted into text columns then paragraphs, text lines and finally words [23].…”

Section: Text / Graphical Segmentationmentioning

confidence: 99%

Research of Artificial Neural Networks Abilities in Printed Words Recognition

Bondarenko¹,

Borisov²

2010

Scientific Journal of Riga Technical University. Computer Sciences

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Abstract-This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conducting a formal comparison of abilities of printed characters recognition between two well known types of second generation neural networks, namely feedforward back-propagation multilayer perceptron (MLP) and Kohonen self-organizing features map (SOM).

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“…The PLA is used as a pre-processing tool that decomposes the mixed-type documents into their main regions. The goal of the PLA is to discover formatting of the text and, from that, to derive meaning associated with the positional and functional blocks in which the text is located (Chauvet et al, 1992;Fujisawa et al, 1992;Schurmann et al, 1992;Witten et al 1994). A PLA method consists of two main steps.…”

Section: Document Multithresholdingmentioning

confidence: 99%