Frank Le Bourgeois

Restoring ink bleed-through degraded document images using a recursive unsupervised classification technique

This paper presents a new method to restore a particular type of degradation related to ancient document images. This degradation, referred to as “bleed-through”, is due to the paper porosity, the chemical quality of the ink, or the conditions of digitalization. It appears as marks degrading the readability of the document image. Our purpose consists then in removing these marks to improve readability. The proposed method is based on a recursive unsupervised segmentation approach applied on the decorrelated data space by the principal component analysis. It generates a binary tree that only the leaves images satisfying a certain condition on their logarithmic histogram are processed. Some experiments, done on real ancient document images provided by the archives of “Chatillon-Chalaronne” illustrate the effectiveness of the suggested method.

DEBORA: Digital AccEss to BOoks of the RenAissance

EBORA (Digital AccEss to BOoks of the RenAissance) is a multidisciplinary European project aiming at digitizing and thus making rare sixteenth century books more accessible. End-users, librarians, historians, researchers in book history and computer scientists participated in the development of remote and collaborative access to digitized Renaissance books, necessary because of the reduced accessibility to digital libraries in image mode through the Internet. The size of files for the storage of images, the lack of a standard file format exchange suitable for progressive transmission, and limited querying possibilities currently limit remote access to digital libraries. To improve accessibility, historical documents must be digitized and retro-converted to extract a detailed description of the image contents suited to users’ needs. Specialists of the Renaissance have described the metadata generally required by end-users and the ideal functionalities of the digital library. The retro-conversion of historical documents is a complex process that includes image capture, metadata extraction, image storage and indexing, automatic conversion in a reusable electronic form, publication on the Internet, and data compression for faster remote access. The steps of this process cannot be developed independently. DEBORA proposes a global approach to retro-conversion from the digitization to the final functionalities of the digital library centered on users’ needs. The retro-conversion process is mainly based on a document image analysis system that simultaneously extracts the metadata and compresses the images. We also propose a file format to describe compressed books as heterogeneous data (images/text/links/ annotation/physical layout and logical structure) suitable for progressive transmission, editing, and annotation. DEBORA is an exploratory project that aims at demonstrating the feasibility of the concepts by developing prototypes tested by end-users.

Automatic Metadata Retrieval from Ancient Manuscripts

The paper presents a document analysis system to retrieve metadata from digitized ancient manuscripts. This platform has been developed to assist researchers, historians and libraries to process a wide variety of manuscripts written in different languages. In order to retrieve different metadata from various digitized documents, we propose a user-training system, which use robust approaches based on a sequential bottom-up process. We develop a low-level segmentation and a basic recognition stage which do not use prior knowledge on documents contents. Our objective was to study the feasibility to process a large variety of manuscripts with the same platform, which can be used by non-specialists in image analysis.

Serialized k-Means for Adaptative Color Image Segmentation

This paper introduces an adaptative segmentation system that was designed for color document image analysis. The method is based on the serialization of a k-means algorithm that is applied sequentially by using a sliding window over the image. During the window’s displacement, the algorithm reuses information from the clusters computed in the previous window and automatically adjusts them in order to adapt the classifier to any new local variation of the colors. To improve the results, we propose to define several different clusters in the color feature space for each logical class. We also reintroduce the user into the initialization step to define the number of classes and the different samples for each class. This method has been tested successfully on ancient color manuscripts, video images and multiple natural and non-natural images having heavy defects and showing illumination variation and transparency. The proposed algorithm is generic enough to be applied on a large variety of images for different purposes such as color image segmentation as well as binarization.

Compression et accessibilité aux images de documents numérisés Application au projet DEBORA

Les bibliotheques numeriques en ligne en mode image souffrent de lacunes et difficultes techniques telles que le manque de metadonnees pertinentes pour une recherche precise des informations, le volume important qu’occupent les images qui limite leur transmission sur le reseau et l’absence d’un format de donnees heterogenes pour la navigation. Dans ce contexte, nous proposons des methodes d’analyse et d’interpretation du contenu des images permettant conjointement de realiser une compression plus efficace et d’extraire automatiquement des metadonnees utiles a l’indexation par le contenu. Notre approche est basee sur une decomposition des images en objets independants qui seront compresses avec des methodes appropriees. Nous proposons ensuite un format de donnees heterogenes, adapte a la navigation dans les ouvrages numerises compresses; il permet de les modifier, de les annoter ou de les echanger sur Internet dans le cadre d’un travail collaboratif.