Using definite clause grammars to build a global system for analyzing collections of documents

International audienceCollections of documents are sets of heterogeneous documents, like a specific ancient book series, having proper structural and semantic properties linking them. A particular collection contains document images with specific physical layouts, like text pages or full-page illustrations, appearing in a specific order. Its contents, like journal articles, may be shared by several pages, not necessary following, producing strong dependencies between pages interpretations.In order to build an analysis system which can bring contextual information from the collection to the appropriate recognition modules for each page, we propose to express the structural and the semantic properties of a collection with a definite clause grammar. This is made possible by representing collections as streams of document descriptors, and by using extensions to the formalism we present here. We are then able to automatically generate a parser dedicated to a collection. Beside allowing structural variations and complex information flows, we also show that this approach enables the design of analysis stages, on a document or a set of documents. The interest of context usage is illustrated with several examples and their appropriate formalization in this framework

Un nouveau système indépendant de rejet multi-seuils pour la reconnaissance de mots manuscrits

In the field of isolated handwritten word recognition, the development of highly effective verification systems to reject words that do not belong to the lexicon or present ambiguities is still an active research topic. In this paper, a novel verification architecture based on support vector machine scoring and on a new algorithm for tuning multiple reject class-dependent thresholds is presented. In essence, a set of support vector machines appended to a standard HMM based recognition system provides class-dependant confidence measures employed by the verification mechanism to accept or reject the recognized hypotheses. In order to tune these class-dependent rejection thresholds, an algorithm based on dynamic programming is proposed which focus on maximizing the recognition rate for a given imposed error rate. Preliminary reported results of experiments carried out on Techno-Vision program RIMES database show that this approach outperforms state of the art rejection methods.En reconnaissance de mots manuscrits, la capacité de rejeter les mots qui n'appartiennent pas au lexique ou présentent une ambiguïté est indispensable pour fiabiliser un système de reconnaissance utilisé en condition réelle. Dans cet article, un nouveau système de rejet reposant sur des classifieurs de type séparateur à vaste marge (SVM) et sur un algorithme original d'apprentissage de seuils multiples est présenté. En substance, nous proposons d'utiliser un ensemble de SVM pour réévaluer les hypothèses produites par un sys-tème de reconnaissance de mots classique à base de modèles de Markov cachés. Le mécanisme de vérification rejette ou accepte ces hypothèses en comparant leur mesure de confiance aux seuils de rejet. Un algorithme optimal de programmation dynamique qui vise à apprendre ces seuils de rejet en maximisant le taux de reconnaissance pour un taux d'erreur désiré est également détaillé. Les expérimentations ont été menées sur la base nationale RIMES du programme Techno-Vision et les résultats présentés montrent la supériorité de cette approche sur les autres méthodes existantes en fiabilité et en performance

Multi-script Baseline Detection Using Perceptive Vision

International audienceBaseline detection is an important step for off-line handwriting recognition of many alphabets. However, it remains a difficult task in the case of varying and irregular handwriting, and sometimes requires script-specific adaptations. In this paper, we propose a new method for baseline detection that is script independent. Indeed, this method is based on the principle of the perceptive vision, which combines several points of view of the same word (from low to high resolution). We validate the genericity of our method by extracting the baseline on three scripts: Arabic, French, and Indian (Bangla), without any adaptation of parameter. We compare our results with other works on the Arabic IFN/ENIT database. Moreover, baseline extraction is often realized independently for each studied word. In this work, we also demonstrate how the use of knowledge on the initial context of the word (text line) significantly improves baseline extraction

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

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

Handwritten word verification by SVM-based hypotheses re-scoring and multiple thresholds rejection

International audienceIn the field of isolated handwritten word recognition, the development of verification systems that optimize the trade-off between performance and reliability is still an active research topic. To minimize the recognition errors, usually, a verification system is used to accept or reject the hypotheses produced by an existing recognition system. In this paper, a novel verification architecture is presented. In essence, the recognition hypotheses, re-scored by a set of the support vector machines, are validated by a verification mechanism based on multiple rejection thresholds. In order to tune these (class-dependent) rejection thresholds, an algorithm based on dynamic programming is proposed which focus on maximizing the recognition rate for a given prefixed error rate

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

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

A novel verification system for handwritten words recognition

International audienceIn the field of isolated handwritten word recognition, the development of highly effective verification systems to reject words presenting ambiguities is still an active research topic. In this paper, a novel verification system based on support vector machine scoring and multiple reject class-dependent thresholds is presented. In essence, a set of support vector machines appended to a standard HMM-based recognition system provides class-dependent confidence measures employed by the verification mechanism to accept or reject the recognized hypotheses. Experimental results on RIMES database show that this approach outperforms other state-of-the-art approaches

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