Véronique Eglin

Document image characterization using a multiresolution analysis of the texture: application to old documents

In this article, we propose a method of characterization of images of old documents based on a texture approach. This characterization is carried out with the help of a multi-resolution study of the textures contained in the images of the document. Thus, by extracting five features linked to the frequencies and to the orientations in the different areas of a page, it is possible to extract and compare elements of high semantic level without expressing any hypothesis about the physical or logical structure of the analyzed documents. Experimentation based on segmentation, data analysis and document image retrieval tools demonstrate the performance of our propositions and the advances that they represent in terms of characterization of content of a deeply heterogeneous corpus.

Document page similarity based on layout visual saliency: application to query by example and document classification

In this paper we propose to define a measure of visualsimilarity to compare different pages in a corpus. Thismeasure is based on the analysis of the visual layoutsaliency of the page composition. This similarity iscomputed using both the document layout andcharacteristics of the text itself. The text characterizationuses statistical features derived from textural primitives.Our purpose is to establish perceptive links betweendocuments in order to facilitate their storage and theirretrieval. In this paper we present two possibleapplications of this measure of similarity: the query ofthe corpus by example and the documents classification.In the first application, we extract documents that are themost visually similar to a document, given as query. Inthe second application, the similarity measure is used toclassify the document under investigation using its visualsimilarity to a reference set of documents. Our test corpusis extracted from the Finland MTDB Oulu multi-genredatabase that provides a great diversity of page layoutsand contents.

Hermite and Gabor transforms for noise reduction and handwriting classification in ancient manuscripts

In this paper, we propose a biologically inspired, global and segmentation free methodology for manuscript noise reduction and classification. Our method consists of developing well-adapted tools for writing enhancement, background noise, text and drawing separation and handwritten patterns characterization with orientation features. We have used here analysis of handwritten images in the spectral domain by frequency decompositions (Hermite transforms) and Gabor filtering for selective text information extraction. We have tested our approach of writing classification on ancient manuscripts corpus, mainly composed of 18th century authors’ documents. The current results are very promising: they show that our biologically inspired methodology can be efficiently used for handwriting analysis without any a priori grapheme segmentation.

A Novel Learning-Free Word Spotting Approach Based on Graph Representation

Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.

Text/graphic labelling of ancient printed documents

This paper presents a text/graphic labelling for ancient printed documents. Our approach is based on the extraction and the quantification of the various orientations that are present in ancient printed document images. The documents are initially cut into normalized square windows in which we analyze significant orientations with a directional rose. Each kind of information (textual or graphical) is typically identified and marked by its orientation distribution. This choice of characterization allows us to separate textual regions from graphics by minimizing the a priori knowledge. The evaluation of our proposition lies on a page classification using layout extraction criteria. The system has been tested over several ancient printed books of the Renaissance.

Writer Identification Using Steered Hermite Features and SVM

Writer recognition is considered as a difficult problem to solve due to variations found in the writing, even from the same writer. In this paper, steered Hermite features are used to identify writer from a written document. We will show that steered Hermite features are highly useful for text images because they extract lot of information, notably for data characterized by oriented features, curves and segments. The algorithm we propose here, first calculates the steered Hermite features of the images which are then passed on to support vector machine for training and testing. The base of tests consists of sample of some lines of writings (five at most) of primarily diversified writings of authors from IAM database. With the proposed algorithm based on steered Hermite features, we were able to achieve an accuracy of around 83% percent for a set of 30 authors with non overlapping images of written text.

Curvelets Based Queries for CBIR Application in Handwriting Collections

This paper presents a new use of the curvelet transform as a multiscale method for indexing linear singularities and curved handwritten shapes in documents images. As it belongs to the wavelet family, this representation can be useful at several scales of details. The proposed scheme for handwritten shape characterization targets to detect oriented and curved fragments at different scales so as to compose an unique signature for each handwritten analyzed samples. In this way, curvelets coefficients are used as a representation tool for handwriting when searching in large manuscripts databases by finding similar handwritten samples. Current results of ancient manuscripts retrieval are very promising with very satisfying precisions and recalls.

A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance

Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.

Text Lines and Snippets Extraction for 19th Century Handwriting Documents Layout Analysis

In this paper we propose a new approach to improve electronic editions of human science corpus, providing an efficient estimation of manuscripts pages structure. In any handwriting documents analysis process, the text line segmentation is an important stage. The presence of variable inter-line spaces, of inconstant base-line skews, overlapping and occlusions in unconstrained ancient 19th handwritten documents complexifies the text lines segmentation task. In this paper, we only use as prior knowledge of script the fact that text lines skews can be random and irregular.In that context, we model text line detection as an image segmentation problem by enhancing text line structure using Hough transform and a clustering of connected components so as to make text line boundaries appear. The proposed approach of snippets decomposition for page layout analysislies on a first step of content pages classification in five visual and genetic taxonomies, and a second step of text line extraction and snippets decomposition. Experiments show that the proposed method achieves high accuracy for detecting text lines in regular and semi-regular handwritten pages in the corpus of digitized Flaubert manuscripts (”Dossiers documentaires de Bouvard et Pécuchet”, 1872-1880).

A Proposition of Retrieval Tools for Historical Document Images Libraries

In this article, we propose a method of characterization of pictures of old documents based on a texture approach. This characterization is carried out with the help of a multi- resolution study of the textures contained in the pictures of the document. So, by extracting five features linked to the frequencies and to the orientations in the different parts of a page, it is possible to extract and to compare elements of high semantic level without expressing any hypothesis about the physical or logical structure of the analysed documents. Experiments show the feasibility of the fulfillment of tools for the navigation or the indexation help. In these experimentations, we will lay the emphasis upon the pertinence of these texture features and the advances that they represent in terms of characterization of content of a deeply heterogeneous corpus.