Vers une approche générique pour la reconnaissance de formes manuscrites structurées : Application aux équations mathématiques et aux caractères chinois

International audienceNous présentons ici une approche générique pour la reconnaissance des formes manuscrites structurées. Leurs caractéristiques : structure 2D complexe, topologie récursive, grand nombre de primitives et de relations spatiales, imposent la mise en oeuvre de stratégies d'analyse adaptées pour limiter l'explosion combinatoire. GASPR est une approche originale consistant à guider la construction et l'exploration d'un graphe de segmentation par des connaissances a priori (grammaire du langage, relations spatiales...). La maîtrise de la complexité est assurée par un algorithme inspiré de A* qui limite la construction du graphe aux meilleures hypothèses. Les expérimentations sur différents types de données en-ligne (équations mathématiques et caractères chinois) confirment l'intérêt de la méthode et sa généricité

Modélisation du positionnement relatif de tracés manuscrits par morphologie mathématique floue

National audienceNous explorons dans ce papier plusieurs approches basées sur la morphologie mathématique floue pour décrire et modéliser le positionnement relatif de tracés manuscrits. Nous montrons d'abord comment les opérations morphologiques floues permettent de définir une partition du plan qui soit adaptée aux spécificités de ces objets imprécis par nature, tout en rendant bien compte de l'ambigüité de leurs relations spatiales. Ensuite, nous proposons une nouvelle approche pour l'apprentissage automatique de modèles de positionnement spatial basée sur ces opérations morphologiques. Enfin, nous illustrons l'apport de ces méthodes pour la reconnaissance de formes par des expérimentations menées sur une base de gestes d'édition en-ligne

Fuzzy relative positioning templates for symbol recognition

International audienceRelative positioning between components of a structured object plays a key role for its interpretation. Fuzzy relative positioning templates are a description framework for 2D handwritten patterns, that is based on positioning models specifically designed for dealing with variability and imprecision of handwriting. In this work, we present fuzzy positioning templates and investigate the idea of recognizing structured handwritten symbols by considering the relative positioning of the components, rather than the shapes of the components themselves or the global shape of the symbol. The templates are automatically trained from data without requiring any prior knowledge. Experiments on a database of on-line symbols prove that this original strategy is a promising approach for interpretation of structured patterns

Learning spatial relationships in hand-drawn patterns using fuzzy mathematical morphology

International audienceWe introduce in this work a new approach for learning spatial relationships between elements of hand-drawn patterns with the help of fuzzy mathematical morphology operators. Relying on mathematical morphology allows to take into account the actual shapes of hand-drawn patterns when modeling their spatial relationships, and thus to cope with the variability of handwriting signal. Extension of mathematical morphology to the fuzzy set framework further allows to handle imprecision of handwriting and to deal with the ambiguity of spatial relationships. The novelty lies in the generative aspect of the models we propose, in the sense that they can exhibit the region of space where the learnt relation is satisfied with respect to a reference object, and can thus be used for driving structural analysis of complex patterns. Experiments over on-line handwritten data show their performance, and prove their ability to deal with variability of handwriting and reasoning under imprecision

Continuous marking menus for learning cursive pen-based gestures

International audienceIn this paper, we present a new type of Marking menus. Continuous Marking Menus are specifically dedicated to pen-based interfaces, and designed to define a set of cursive, realistic handwritten gestures. In menu mode, they offer a continuous visual feedback and fluent exploration of menu hierarchy, inviting the user to execute cursive gestures for invoking the desired commands. In marking mode, a specific gesture recognition method is proposed and proved to be very efficient for recognizing cursive gestures

Explicit fuzzy modeling of shapes and positioning for handwritten Chinese character recognition

International audienceIn this paper, we present a new method for on-line Chinese character recognition that relies on an explicit description of characters structure. Contrary to most of known structural approaches, this model can describe characters written in a fluent style, thanks to a flexible fuzzy modeling of shapes and positioning of their structural components (primitives and radicals). We designed a process for incremental training of the models cooperated with automatic structural labeling for minimizing the required manual task in model design. First experiments show that the method is able to recognize non-regularly written characters and has a convincing generalization ability

Special Radical Detection by Statistical Classification for On-line Handwritten Chinese Character Recognition

International audienceThe hierarchical nature of Chinese characters has inspired radical-based recognition, but radical segmentation from characters remains a challenge. We previously proposed a radical-based approach for on-line handwritten Chinese character recognition, which incorporates character structure knowledge into integrated radical segmentation and recognition, and performs well on characters of left-right and up-down structures (non-special structures). In this paper, we propose a statistical-classification-based method for detecting special radicals from special-structure characters. We design 19 binary classifiers for classifying candidate radicals (groups of strokes) hypothesized from the input character. Characters with special radicals detected are recognized using special-structure models, while those without special radicals are recognized using the models for non-special structures. We applied the recognition framework to 6,763 character classes, and achieved promising recognition performance in experiments

The ILGDB database of realistic pen-based gestural commands

International audienceIn this paper, we introduce the Intuidoc-Loustic Gestures DataBase (ILGDB), a new publicly available database of realistic pen-based gestures for evaluation of recognition systems in pen-enabled interfaces. ILGDB was collected in a real world context and in an immersive environment. As it contains a large number of unconstrained user-defined gestures, ILGDB offers a unique diversity of content that is likely to serve as a precious tool for benchmarking of gesture recognition systems. We report first baseline experimental results on the task of Writer-Dependent gesture recognition

Evaluation of Continuous Marking Menus for Learning Cursive Pen-based Commands

International audienceWe present here the Continuous Marking Menus, which help users learning a set of handwritten commands on a pen-based interface. The aim of this paper is to experimentally attest the interest of this new type of menu by evaluating its ability to help the learning of a set of gestures. We describe an experimental comparison on the task of learning a set of gestures with or without the help of Continuous Marking Menus, and we conclude that with the help of Continuous Marking Menus, people learn more easily the gestures

Méta-modèles de positionnement spatial pour la reconnaissance de tracés manuscrits

The rise of pen-enabled interfaces is supported by the development of automatic methods for interpretation of more and more rich and complex input data: handwritten text, mathematical equations, sketches, free handwritten notes... For efficiently recognizing these handwritten documents, one has to consider jointly the shapes of their components and the relative positioning between them. Our research focuses on the modeling of relative positioning between handwritten objects, considering that the potential of this part of the information is not fully exploited in the current methods. We introduce spatial meta-templates, a generic modeling for describing spatial relations between objects of diverse nature, complexity, and shape. These models can be trained from data and provide richer and more accurate descriptions because they authorize to reason about spatial information directly in the image space. Relying on fuzzy sets theory and mathematical morphology allows dealing with imprecision and offers intuitive description of spatial relations. A meta-template is endowed with a prediction capacity: it provides the description of modeled spatial relations with respect to a reference object in the image, as a spatial template. This enables to conduct segmentation of objects depending on their spatial context. By exploiting these models, we present a new representation for structured handwritten objects. It relies only on modeling of the spatial information so as to demonstrate the importance of spatial information for interpretation of these objects. The segmentation of handwritten strokes into structural primitives is driven by positioning models, making use of their prediction ability. Experimental results, obtained with objects of diverse nature and complexity (Chinese characters, editing gestures, mathematical symbols, letters), validate the quality of positioning description offered by our models. The performance on the task of recognizing symbols with a spatial-based representation further attests the importance of this information and confirms the ability of meta-templates to model it properly and accurately. These results both show the richness of spatial information and give an insight on the potential of meta-templates for improving methods for handwritten document interpretation.L'essor des interfaces homme-machine permettant la saisie d'informations à l'aide d'un stylo électronique est accompagné par le développement de méthodes automatiques pour interpréter des données de plus en plus riches et complexes : texte manuscrit, mais aussi expressions mathématiques, schémas, prise de notes libre... Pour interpréter efficacement ces documents manuscrits, il est nécessaire de considérer conjointement les formes des objets qui les constituent et leur positionnement spatial. Nos recherches se concentrent sur la modélisation du positionnement spatial entre des objets manuscrits, en partant du constat qu'il n'est pas exploité dans toute sa richesse par les méthodes actuelles. Nous introduisons le concept de méta-modèle spatial, une modélisation générique pour décrire des relations spatiales entre des objets de nature, complexité et formes variables. Ces modèles, qui peuvent être appris à partir de données, offrent une richesse et une précision inédite car ils autorisent la conduite d'un raisonnement spatial directement dans l'espace image. L'appui sur le cadre de la théorie des sous-ensembles flous et de la morphologie mathématique permet la gestion de l'imprécision et offre une description des relations spatiales conforme à l'intuition. Un méta-modèle est doté d'un pouvoir de prédiction qui permet de décrire la relation spatiale modélisée au sein de l'image, par rapport à un objet de référence. Cette capacité rend possible la visualisation des modèles et fournit un outil pour segmenter les tracés en fonction de leur contexte. En exploitant ces modèles, nous proposons une représentation pour des objets manuscrits à la structure complexe. Cette représentation repose uniquement sur la modélisation de leurs informations spatiales, afin de démontrer l'importance de ces informations pour l'interprétation d'objets manuscrits structurés. La segmentation des tracés en primitives structurelles est guidée par les modèles de positionnement, via leur capacité de prédiction. Les résultats expérimentaux, portant sur des objets de complexité et de natures diverses (caractères chinois, gestes d'édition, symboles mathématiques, lettres), confirment la bonne qualité de description du positionnement offerte par les méta-modèles. Les tests de reconnaissance de symboles par l'exploitation de leur information spatiale attestent d'une part de l'importance de cette information et valident d'autre part la capacité des méta-modèles à la représenter avec une grande précision. Ces résultats témoignent donc de la richesse de l'information spatiale et du potentiel des méta-modèles spatiaux pour l'amélioration des techniques de traitement du document manuscrit