Dark Energy and the Return of the Phoenix Universe

In cyclic universe models based on a single scalar field (e.g., the radion determining the distance between branes in M-theory), virtually the entire universe makes it through the ekpyrotic smoothing and flattening phase, bounces, and enters a new epoch of expansion and cooling. This stable evolution cannot occur, however, if scale-invariant curvature perturbations are produced by the entropic mechanism because it requires two scalar fields (e.g., the radion and the Calabi-Yau dilaton) evolving along an unstable classical trajectory. In fact, we show here that an overwhelming fraction of the universe fails to make it through the ekpyrotic phase; nevertheless, a sufficient volume survives and cycling continues forever provided the dark energy phase of the cycle lasts long enough, of order a trillion years. Two consequences are a new role for dark energy and a global structure of the universe radically different from that of eternal inflation.Comment: 5 pages, 3 figure

Text Line Extraction in Handwritten Document with Kalman Filter Applied on Low Resolution Image

International audienceIn this paper we present a method to extract text lines in handwritten documents. Indeed, line extraction is a first interesting step in document structure recognition. Our method is based on a notion of perceptive vision: at a certain distance, text lines of documents can be seen as line segments. Therefore, we propose to detect text line using a line segment extractor on low resolution images. We present our extractor based on the theory of Kalman filtering. Our method makes it possible to deal with difficulties met in ancient damaged documents: skew, curved lines, overlapping text lines. . .We present results on archive documents from the 18th and 19th century

Un bibliothécaire modèle ?

Intervention au colloque sur "L\u27histoire des bibliothécaires" organisé par le Centre de recherche en histoire du livre à la Bibliothèque municipale de Lyon du 27 au 29 novembre 2003. Rétrospective de la vie de Bernard Itier, bibliothécaire à l’abbaye Saint-Martial de Limoges entre 1195 et 1225

Interest of perceptive vision for document structure analysis

International audienceThis work addresses the problem of document image analysis, and more particularly the topic of document structure recognition in old, damaged and handwritten document. The goal of this paper is to present the interest of the human perceptive vision for document analysis. We focus on two aspects of the model of perceptive vision: the perceptive cycle and the visual attention. We present the key elements of the perceptive vision that can be used for document analysis. Thus, we introduce the perceptive vision in an existing method for document structure recognition, which enable both to show how we used the properties of the perceptive vision and to compare the results obtained with and without perceptive vision. We apply our method for the analysis of several kinds of documents (archive registers, old newspapers, incoming mails . . . ) and show that the perceptive vision signicantly improves their recognition. Moreover, the use of the perceptive vision simplies the description of complex documents. At last, the running time is often reduced

Use of Perceptive Vision for Rulling Recognition in Ancient Documents

International audienceRulings are graphical primitives that are essential for document structure recognition. However in the case of ancient documents, bad printing techniques or bad conditions of conservation induce problems for their recognition. Consequently, usual line segment extractors are not powerful enough to properly extract all the rulings of a heterogeneous document. In this paper, we propose a new method for ruling recognition, based on perceptive vision: we show that combining several levels of vision improves ruling recognition. Thus, it is possible to put forward hypothesis on the nature of the rulings at a given resolution, and to confirm or infirm their presence and find their exact position at higher resolutions. We propose an original strategy of cooperation between resolutions and present tools to set up a correspondence between the elements extracted at each resolution. We validate this approach on images of ancient newspaper pages (dated between 1848 and 1944). At last, we propose to use the extracted rulings for the structure analysis of newspaper pages. We show that using more reliable extracted rulings simplifies and improves document structure recognition

Use of Perceptive Vision for Ruling Recognition in Ancient Documents

Rulings are graphical primitives that are essential for document structure recognition. However in the case of ancient documents, bad printing techniques or bad conditions of conservation induce problems for their ecient recognition. Consequently, usual line segment extractors are not powerful enough to properly extract all the rulings of a heterogeneous document. In this paper, we propose a new method for ruling recognition, based on perceptive vision: we show that combining several levels of vision improves ruling recognition. Thus, it is possible to put forward hypothesis on the nature of the rulings at a given resolution, and to conrm or inrm their presence and nd their exact position at higher resolutions. We propose an original strategy of cooperation between resolutions and present tools to set up a correspondence between the elements extracted at each resolution. We validate this approach on images of ancient newspaper pages (dated between 1848 and 1944). We also propose to use the extracted rulings for the structure analysis of newspaper pages. We show that using more reliable extracted rulings simplies and improves document structure recognition

A perceptive method for handwritten text segmentation

International audienceThis paper presents a new method to address the problem of handwritten text segmentation into text lines and words. Thus, we propose a method based on the cooperation among points of view that enables the localization of the text lines in a low resolution image, and then to associate the pixels at a higher level of resolution. Thanks to the combination of levels of vision, we can detect overlapping characters and re-segment the connected components during the analysis. Then, we propose a segmentation of lines into words based on the cooperation among digital data and symbolic knowledge. The digital data are obtained from distances inside a Delaunay graph, which gives a precise distance between connected components, at the pixel level. We introduce structural rules in order to take into account some generic knowledge about the organization of a text page. This cooperation among information gives a bigger power of expression and ensures the global coherence of the recognition. We validate this work using the metrics and the database proposed for the segmentation contest of ICDAR 2009. Thus, we show that our method obtains very interesting results, compared to the other methods of the literature. More precisely, we are able to deal with slope and curvature, overlapping text lines and varied kinds of writings, which are the main diculties met by the other methods

A generic method for structure recognition of handwritten mail documents

International audienceThis 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