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

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 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

Mémoire visuelle pour l'analyse de documents structurés

International audienceCurrent analysis methods propose systems which are adapted to recognize sets of documents of a same kind (statistical methods, grammatical analysis), but each page is then processed in isolation. However, in a collection context, it is important to make the most of the result of the processing of one or several pages in order to improve the later processing of other pages. Thus, we propose the concept of visual memory so as to enable existing recognition systems to use, locally and at run time, contextual information for each page. We detail its specifications which allow an implementation in any recognition system. Finally, we present our implementation using a grammatical method, and its application for several types of information flow.Les méthodes d'analyse de documents actuelles proposent des systèmes qui sont adaptés pour reconnaitre des lots de documents d'un même type (méthodes statistiques, analyses grammaticales), mais qui ne sont ensuite appliqués qu'isolément sur chacune des pages à traiter. Pourtant, dans un contexte de collection, il est important de profiter du résultat du traitement d'une ou plusieurs pages pour améliorer le traitement ultérieur d'autres pages. Nous proposons donc un cadre, la mémoire visuelle, pour intégrer aux systèmes de reconnaissance existants la prise en compte locale et en cours d'analyse d'informations contextuelles utiles pour chaque page. Nous détaillons ses spécifications qui permettent son implémentation dans n'importe quel système de reconnaissance. Nous présentons enfin une mise en œuvre dans un système grammatical et déclinons différents schémas de circulation d'information permis par la mémoire visuelle

Perceptive Vision for Headline Localisation in Bangla Handwritten Text Recognition

International audienceIn this paper, we propose to give tools for Bangla handwriting recognition. We present a mechanism to segment documents into text lines and words, and more specifically to detect headline position in each word. Indeed, this headline is an horizontal line on the upper part of most of characters, which is characteristic of Bangla writing. Its localisation is a new approach that can improve text recognition quality. This headline is detected into words inside text lines thanks to a notion of perceptive vision: at a certain distance, text lines appear as line-segments that give the global orientation of words. Watching closer may help to give the exact position of the headline. Consequently, this work is mainly based on applying a segment extractor at different image resolutions and combining extracted information in order to compute the headlines. Our line-segment extractor is based on Kalman filtering

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

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

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

Using a neighbourhood graph based on Voronoï tessellation with DMOS, a generic method for structured document recognition

To develop a method for structured document recognition, it is necessary to know the relative position of the graphical elements in a document. In order to deal with this notion, we build a neighbourhood graph based on Vorono¨ı tessellation. We propose to combine the use of this interesting notion of neighbourhood with an existing generic document recognition method, DMOS, which has been used to describe various kinds of documents. This association allows exploiting different aspects of the neighbourhood graph, separating the graph analysis from the knowledge linked to a kind of document, and establishing a bidirectional context-based relation between the analyser and the graph. We apply this method on the analysis of various documents

Multiresolution cooperation makes easier document structure recognition

International audienceThis paper shows the interest of imitating the perceptive vision to improve the recognition of the structure of ancient, noisy and low structured documents. The perceptive vision, that is used by human eye, consists in focusing attention on interesting elements after having detecting their presence in a global vision process. We propose a generic method in order to apply this concept to various problems and kinds of documents. Thus, we introduce the concept of cooperation between multiresolution visions into a generic method. The originality of this work is that the cooperation between resolutions is totally led by the knowledge dedicated to each kind of document. In this paper, we present this method on three kinds of documents: handwritten low structured mail documents, naturalization decree register that are archive noisy documents from the 19th century and Bangla script that requires a precise vision. This work is validated on 86,291 documents