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PhD : Contextual segmentation of document images using greylevel texture analysis

PhD : Contextual segmentation of document images using greylevel texture analysis: 3 years PhD position held at the Computing Science Department of ESIEE Paris

Description : Further to the acceptance of a French Ministry of Industry funded project, applications are invited for a 3 years PhD position held at the Computing Science Department of ESIEE Paris. For an overview of the activities of the Department, please visit: http://www.esiee.fr/en/research/a2si.phpThe overall goal of the project is to improve over an existing document analysis system, which is able to convert various type of documents, most of them originating from the French heritage and assuming various physical forms (books, microfilms, postcards, civil deeds, etc.). The novelty of the PhD comes from the proposed methodology, which suggests processing images directly in grey levels rather than in their binarized version. Strong knowledges in image analysis and processing, as well as the technical mastery of a programming language (such as C++ or Java) are a must. Experience with an environment such as Matlab or R, and further knowledges on statistical classification/recognition methods (SVM, neural networks, bayesian networks, …) would be additional assets. Salary : 38 k€ / annum. Expected starting date : november 2008. Interested applicants should be either French-speaking or fluent in english. Resumes and motivation letters should be sent by e-mail only to : x.hilaire (at) esiee.fr PhD : Contextual segmentation of document images using greylevel texture analysis Document page segmentation is to automatically recognize and extract its various components (text and text blocks, mathematical formulas, halftones, captions, …). Numerous segmentation methods are available in the literature. The usual taxonomy grossly fits them into three families : top-down methods (one starts from an entire page, then recursively split it until a criterion is satisfied on each region), bottom-up methods (the opposite approach), and hybrid methods. The latter family obviously gathers methods that take advantage of both top-down and bottom-up strategies, but also those which rely on texture analysis (Gabor analysis, co-occurrence, HTD, edge histograms, etc.). One critic that may be addressed to almost all of the existing methods is their inability to process any image but binary ones, as most of them generally need to separate the background from the foreground of the document very early. The aim of the PhD is to design a texture-based method, which would improve over the existing ones in three different manners : 1. By using greylevel texture descriptors : the additional information conveyed by greylevels should result in a significant accuracy of the descriptors, and thereafter in that of the segmentation itself. It would even be desirable to define or use color whenever color is available. 2. By contextualizing the segmentation : although heterogeneous, the document corpus remains rather well identified. Our idea is then to introduce document models that could not only permit to modify the probability laws that a pixel belongs to a class given the document and the corpus, but also to give an a posteriori explanation of these laws taken jointly. Bayesian networks, in particular, could constitute an appealing framework to solve this problem. . 3. By explicitly modelizing and explaining noise : it is highly desirable that the segmentation method modelize noise as a class of its own, and be able to explain it. Such an approach has already been proposed in the literature, for instance for distinguishing between handwritten and printed text on binary images by Zheng et al., and exhibited interesting results. Significant improvements are to be expected by a similar approach extended in greylevels.

Homepage :
Category : Computer science
Contact address : ESIEE Paris Cité Descartes 2 bd Blaise Pascal 93162 Noisy-le-Grand FRANCE
Keywords : document,image,analysis,segmentation,texture,statistics
Your Name : Xavier HILAIRE
Email : x.hilaire@esiee.fr

Tags: Analysis, Computer, Contextual, document, France, greylevel, Image, images, PhD, segmentation, Statistics, texture, using

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