Illustrations Segmentation in Digitized Documents Using Local Correlation Features

In this paper we propose an approach for Document Layout Analysis based on local correlation features. We identify and extract illustrations in digitized documents by learning the discriminative patterns of textual and pictorial regions. The proposal has been demonstrated to be effective on historical datasets and to outperform the state-of-the-art in presence of challenging documents with a large variety of pictorial elements

Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections

Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections details and analyzes the first stage of work of the Image Analysis for Archival Discovery project team. Our team is is investigating the use of image analysis to identify poetic content in historic newspapers. The project seeks both to augment the study of literary history by drawing attention to the magnitude of poetry published in newspapers and by making the poetry more readily available for study, as well as to advance work on the use of digital images in facilitating discovery in digital libraries and other digitized collections. We have recently completed the process of training our classifier for identifying poetic content, and as we prepare to move in to the deployment stage, we are making available our methods for classification and testing in order to promote further research and discussion. The precision and recall values achieved during the training (90.58%; 79.4%) and testing (74.92%; 61.84%) stages are encouraging. In addition to discussing why such an approach is needed and relevant and situating our project alongside related work, this paper analyzes preliminary results, which support the feasibility and viability of our approach to detecting poetic content in historic newspaper collections

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv

Analyse d’images de documents patrimoniaux : une approche structurelle à base de texture

Over the last few years, there has been tremendous growth in digitizing collections of cultural heritage documents. Thus, many challenges and open issues have been raised, such as information retrieval in digital libraries or analyzing page content of historical books. Recently, an important need has emerged which consists in designing a computer-aided characterization and categorization tool, able to index or group historical digitized book pages according to several criteria, mainly the layout structure and/or typographic/graphical characteristics of the historical document image content. Thus, the work conducted in this thesis presents an automatic approach for characterization and categorization of historical book pages. The proposed approach is applicable to a large variety of ancient books. In addition, it does not assume a priori knowledge regarding document image layout and content. It is based on the use of texture and graph algorithms to provide a rich and holistic description of the layout and content of the analyzed book pages to characterize and categorize historical book pages. The categorization is based on the characterization of the digitized page content by texture, shape, geometric and topological descriptors. This characterization is represented by a structural signature. More precisely, the signature-based characterization approach consists of two main stages. The first stage is extracting homogeneous regions. Then, the second one is proposing a graph-based page signature which is based on the extracted homogeneous regions, reflecting its layout and content. Afterwards, by comparing the different obtained graph-based signatures using a graph-matching paradigm, the similarities of digitized historical book page layout and/or content can be deduced. Subsequently, book pages with similar layout and/or content can be categorized and grouped, and a table of contents/summary of the analyzed digitized historical book can be provided automatically. As a consequence, numerous signature-based applications (e.g. information retrieval in digital libraries according to several criteria, page categorization) can be implemented for managing effectively a corpus or collections of books. To illustrate the effectiveness of the proposed page signature, a detailed experimental evaluation has been conducted in this work for assessing two possible categorization applications, unsupervised page classification and page stream segmentation. In addition, the different steps of the proposed approach have been evaluated on a large variety of historical document images.Les récents progrès dans la numérisation des collections de documents patrimoniaux ont ravivé de nouveaux défis afin de garantir une conservation durable et de fournir un accès plus large aux documents anciens. En parallèle de la recherche d'information dans les bibliothèques numériques ou l'analyse du contenu des pages numérisées dans les ouvrages anciens, la caractérisation et la catégorisation des pages d'ouvrages anciens a connu récemment un regain d'intérêt. Les efforts se concentrent autant sur le développement d'outils rapides et automatiques de caractérisation et catégorisation des pages d'ouvrages anciens, capables de classer les pages d'un ouvrage numérisé en fonction de plusieurs critères, notamment la structure des mises en page et/ou les caractéristiques typographiques/graphiques du contenu de ces pages. Ainsi, dans le cadre de cette thèse, nous proposons une approche permettant la caractérisation et la catégorisation automatiques des pages d'un ouvrage ancien. L'approche proposée se veut indépendante de la structure et du contenu de l'ouvrage analysé. Le principal avantage de ce travail réside dans le fait que l'approche s'affranchit des connaissances préalables, que ce soit concernant le contenu du document ou sa structure. Elle est basée sur une analyse des descripteurs de texture et une représentation structurelle en graphe afin de fournir une description riche permettant une catégorisation à partir du contenu graphique (capturé par la texture) et des mises en page (représentées par des graphes). En effet, cette catégorisation s'appuie sur la caractérisation du contenu de la page numérisée à l'aide d'une analyse des descripteurs de texture, de forme, géométriques et topologiques. Cette caractérisation est définie à l'aide d'une représentation structurelle. Dans le détail, l'approche de catégorisation se décompose en deux étapes principales successives. La première consiste à extraire des régions homogènes. La seconde vise à proposer une signature structurelle à base de texture, sous la forme d'un graphe, construite à partir des régions homogènes extraites et reflétant la structure de la page analysée. Cette signature assure la mise en œuvre de nombreuses applications pour gérer efficacement un corpus ou des collections de livres patrimoniaux (par exemple, la recherche d'information dans les bibliothèques numériques en fonction de plusieurs critères, ou la catégorisation des pages d'un même ouvrage). En comparant les différentes signatures structurelles par le biais de la distance d'édition entre graphes, les similitudes entre les pages d'un même ouvrage en termes de leurs mises en page et/ou contenus peuvent être déduites. Ainsi de suite, les pages ayant des mises en page et/ou contenus similaires peuvent être catégorisées, et un résumé/une table des matières de l'ouvrage analysé peut être alors généré automatiquement. Pour illustrer l'efficacité de la signature proposée, une étude expérimentale détaillée a été menée dans ce travail pour évaluer deux applications possibles de catégorisation de pages d'un même ouvrage, la classification non supervisée de pages et la segmentation de flux de pages d'un même ouvrage. En outre, les différentes étapes de l'approche proposée ont donné lieu à des évaluations par le biais d'expérimentations menées sur un large corpus de documents patrimoniaux

Character recognition and information retrieval

Presented are two technologies, character recognition and information retrieval, that are used for text processing. Character recognition translates text image data to a computer-coded format; information retrieval stores these data and provides efficient access to the text. The necessity of their eventual coupling is obvious. Their sequential application though (with no manual intervention) has been considered impractical at best. Our experimentation exploits these two technologies in just this way. We identify problems with their combined use, as well as show that the technologies have come to a point where they can be applied in succession

Document preprocessing and fuzzy unsupervised character classification

This dissertation presents document preprocessing and fuzzy unsupervised character classification for automatically reading daily-received office documents that have complex layout structures, such as multiple columns and mixed-mode contents of texts, graphics and half-tone pictures. First, the block segmentation algorithm is performed based on a simple two-step run-length smoothing to decompose a document into single-mode blocks. Next, the block classification is performed based on the clustering rules to classify each block into one of the types such as text, horizontal or vertical lines, graphics, and pictures. The mean white-to-black transition is shown as an invariance for textual blocks, and is useful for block discrimination. A fuzzy model for unsupervised character classification is designed to improve the robustness, correctness, and speed of the character recognition system. The classification procedures are divided into two stages. The first stage separates the characters into seven typographical categories based on word structures of a text line. The second stage uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. A fuzzy model of unsupervised character classification, which is more natural in the representation of prototypes for character matching, is defined and the weighted fuzzy similarity measure is explored. The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character recognition procedure is simply applied on the limited versions of the fuzzy prototypes. To avoid information loss and extra distortion, an topography-based approach is proposed to apply directly on the fuzzy prototypes to extract the skeletons. First, a convolution by a bell-shaped function is performed to obtain a smooth surface. Second, the ridge points are extracted by rule-based topographic analysis of the structure. Third, a membership function is assigned to ridge points with values indicating the degrees of membership with respect to the skeleton of an object. Finally, the significant ridge points are linked to form strokes of skeleton, and the clues of eigenvalue variation are used to deal with degradation and preserve connectivity. Experimental results show that our algorithm can reduce the deformation of junction points and correctly extract the whole skeleton although a character is broken into pieces. For some characters merged together, the breaking candidates can be easily located by searching for the saddle points. A pruning algorithm is then applied on each breaking position. At last, a multiple context confirmation can be applied to increase the reliability of breaking hypotheses

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Adaptive restoration of text images containing touching and broken characters

For document processing systems, automated data entry is generally performed by optical character recognition (OCR) systems. To make these systems practical, reliable OCR systems are essential. However, distortions in document images cause character recognition errors, thereby, reducing the accuracy of OCR systems. In document images, most OCR errors are caused by broken and touching characters. This thesis presents an adaptive system to restore text images distorted by touching and broken characters. The adaptive system uses the distorted text image and the output from an OCR system to generate the training character image. Using the training image and the distorted image, the system trains an adaptive restoration filter and then uses the trained filter to restore the distorted text image. To demonstrate the performance of this technique, it was applied to several distorted images containing touching or broken characters. The results show that this technique can improve both pixel and OCR accuracy of distorted text images containing touching or broken characters