Nicholas Journet

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.

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.

The ICDAR/GREC 2013 Music Scores Competition: Staff Removal

The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.

ICDAR 2013 Music Scores Competition: Staff Removal

The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participants methods and the obtained results.

Semi-synthetic Document Image Generation Using Texture Mapping on Scanned 3D Document Shapes

This article presents a method for generating semi-synthetic images of old documents where the pages might be torn (not flat). By using only 2D deformation models, most existing methods give non-realistic synthetic document images. Thus, we propose to use 3D approach for reproducing geometric distortions in real documents. First, a new proposed texture coordinate generation technique extracts texture coordinates of each vertex in the document shape (mesh) resulting from 3D scanning of a real degraded document. Then, any 2D document image can be overlayed on the mesh by using an existing texture image mapping method. As a result, many complex real geometric distortions can be integrated in generated synthetic images. These images then can be used for enriching training sets or for performance evaluation. The degradation method here is jointly used with the character degradation model we proposed in [1] to generate the 6000 semi-synthetic degraded images of the music score removal staff line competition of ICDAR 2013.

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.

Ancient printed documents indexation: a new approach

Based on the study of the specificity of historical printed books and on the main error sources of classical methods of page layout analysis, this paper presents a new way to achieve an indexation of ancient printed documents. We have developed an approach based on the extraction and the quantification of the various orientations that are present in printed document images. The documents are initially splitted into homogenous areas in which we analyze significant orientations with a directional rose. Each kind of information (textual or graphical) is typically identified and labelled according to its orientation distribution. This choice of characterization allows us to separate textual regions from graphical ones by minimizing the a priori knowledge. The evaluation of our proposition lies on a document image retrieval using layout extraction criteria and can also be used to precisely localize graphical parts in various types of documents. The system has been tested with success over several ancient printed books of the Renaissance.

Quality Evaluation of Ancient Digitized Documents for Binarization Prediction

This article proposes an approach to predict the result of binarization algorithms on a given document image according to its state of degradation. Indeed, historical documents suffer from different types of degradation which result in binarization errors. We intend to characterize the degradation of a document image by using different features based on the intensity, quantity and location of the degradation. These features allow us to build prediction models of binarization algorithms that are very accurate according to R2 values and p-values. The prediction models are used to select the best binarization algorithm for a given document image. Obviously, this image-by-image strategy improves the binarization of the entire dataset.

Dedicated texture based tools for characterisation of old books

This paper deals with the development of suitable assistance tools for humanists and historians to help them retrieve information documents. This paper represents a part of this ambitious project and deals with the design of a pixel classification method for ancient typewritten documents. The approach presented here is based on a multiresolution map construction and analysis; for five resolutions we construct five different characterisation maps. All the maps are based on texture information (correlation of pixels orientations, grey level pixel density: etc). After the merging of these 25 maps, each pixel of the original image is described by a vector which allows the computing of a hierarchical classification. In order to avoid issues linked to the binarization process, ail maps are computed for grey level images. The system has been tested on a CESR database of ancient printed books of the Renaissance. The classification results are evaluated through several visual classification illustrations

Semi-structured document image matching and recognition

This article presents a method to recognize and to localize semi-structured documents such as ID cards, tickets, invoices, etc. Standard object recognition methods based on interest points work well on natural images but fail on document images because of repetitive patterns like text. In this article, we propose an adaptation of object recognition for image documents. The advantages of our method is that it does not use character recognition or segmentation and it is robust to rotation, scale, illumination, blur, noise and local distortions. Furthermore, tests show that an average precision of 97.2% and recall of 94.6% is obtained for matching 7 different kinds of documents in a database of 2155 documents.