Apprentissage de relations spatiales pour la reconnaissance d'expressions mathématiques manuscrites en-ligne

International audienceNous proposons dans cet article une nouvelle méthode d'analyse syntaxique et structurelle pour un système de reconnaissance d'expressions mathématiques manuscrites enligne. Une grammaire probabiliste est mise en place pour regrouper les hypothèses de segmentation/reconnaissance proposées par un segmenteur 2D. Les probabilités associés à la grammaire sont calculées à partir d'informations structurelles entre les symboles. Ces informations structurelles utilisent l'évaluation d'une relation spatiale entre les éléments intervenants dans chaque règle. L'apprentissage du système se fait en deux phases, d'abord l'apprentissage global du classifieur sans tenir compte de la grammaire, puis l'apprentissage des relations spatiales intervenant dans la grammaire. Ce système est entraîné et testé sur une large base synthétisée d'expressions, puis testé sur une base d'expressions réelles complexes

Neighborhood Label Extension for Handwritten/Printed Text Separation in Arabic Documents

International audienceThis paper addresses the problem of handwritten and printed text separation in Arabic document images. The objective is to extract handwritten text from other parts of the document. This allows the application, in a second time, of a specialized processing on the extracted handwritten part or even on the printed one. Documents are first preprocessed in order to remove eventual noise and correct document orientation. Then, the document is segmented into pseudo-lines that are segmented in turn into pseudo-words. A local classification step, using a Gaussian kernel SVM, associates each pseudo-word into handwritten or printed classes. This label is then propagated in the pseudo-word's neighborhood in order to recover from classification errors. The proposed methodology has been tested on a set of public real Arabic documents achieving a separation rate of around 90%

Complex Document Classification and Localization Application on Identity Document Images

International audienceThis paper studies the problem of document image classification. More specifically, we address the classification of documents composed of few textual information and complex background (such as identity documents). Unlike most existing systems, the proposed approach simultaneously locates the document and recognizes its class. The latter is defined by the document nature (passport, ID, etc.), emission country, version, and the visible side (main or back). This task is very challenging due to unconstrained capturing conditions, sparse textual information, and varying components that are irrelevant to the classification, e.g. photo, names, address, etc. First, a base of document models is created from reference images. We show that training images are not necessary and only one reference image is enough to create a document model. Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. The matching process is optimized to guarantee an execution time independent from the number of document models. Once the document model is found, a more accurate matching is performed to locate the document and facilitate information extraction. Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%

Reconnaissance de structures bidimensionnelles : Application aux expressions mathématiques manuscrites en-ligne

This thesis focuses on the study, conception, development and testing of a recognition system for two-dimensional handwritten structures. The proposed system is based on a global architecture that considers the problem of recognition as simultaneous optimization of segmentation, symbol recognition, and interpretation. In this framework, we have first designed a system to recognize handwritten mathematical expression. All the three problems of segmentation, recognition and interpretation are not straightforward. Segmentation is complex because of the large freedom for composing an expression, since delayed multi-stroke symbols are considered. Recognition has to face a large number of classes, and to deal with the problem of unknown pattern, and interpretation suffers for the fuzzy nature of spatial relationships. We have defined a solution which minimizes a global cost function where recognition costs and structural costs are combined and a large exploration of the space of solutions is proposed. The results are very promising and competitive compared to those of the literature. We have finally shown the generality of our approach in adapting the system to the recognition of another 2D language, which is used to design handwritten flowcharts.Les travaux présentés dans le cadre de cette thèse portent sur l'étude, la conception, le développement et le test d'un système de reconnaissance de structures manuscrites bidimensionnelles. Le système proposé se base sur une architecture globale qui considère le problème de reconnaissance en tant qu'optimisation simultanée de la segmentation, de la reconnaissance de symboles, et de l'interprétation. Le premier cadre d'applications a été celui d'un système de reconnaissance d'expressions mathématiques manuscrites. La difficulté du problème se situe aux trois niveaux évoqués. La segmentation est complexe du fait de la grande liberté de composition d'une expression, avec notamment la possibilité de symboles multi-traits non séquentiels ; la reconnaissance doit affronter un nombre élevé de classes et en particulier, gérer les situations de formes non-apprises ; l'interprétation peut-être ambiguë du fait du positionnement spatial approximatif. La solution proposée repose sur la minimisation d'une fonction de coût global qui met en compétition des coûts de reconnaissance et des coûts structurels pour explorer un vaste espace de solutions. Les résultats obtenus sont très compétitifs et prometteurs comparés à ceux de la littérature. Nous avons finalement montré la généricité de notre approche en l'adaptant à la reconnaissance d'un autre type de langage 2D, celui des représentations graphiques de type organigramme

Reconnaissance de structures bidimensionnelles (application aux expressions mathématiques manuscrites)

NANTES-BU Sciences (441092104) / SudocSudocFranceF

The Problem of Handwritten Mathematical Expression Recognition Evaluation

International audienceWe discuss in this paper some issues related to the problem of mathematical expression recognition. The very first important issue is to define how to ground truth a dataset of handwritten mathematical expressions, and next we have to face the problem of benchmarking systems. We propose to define some indicators and the way to compute them so as they reflect the actual performances of a given syste

Improving online handwritten mathematical expressions recognition with contextual modeling

International audienceWe propose in this paper a new contextual modelling method for combining syntactic and structural information for the recognition of online handwritten mathematical expressions. Those models are used to find the most likely combination of segmentation/recognition hypotheses proposed by a 2D segmentor. Models are based on structural information concerning the layouts of symbols. They are learned from a mathematical expressions dataset to prevent the use of heuristic rules which are fuzzy by nature. The system is tested with a large base of synthetic expressions and also with a set of real complex expression

Handwritten/printed text separation Using pseudo- lines for contextual re-labeling

International audience—This paper addresses the problem of machine printed and handwritten text separation in real noisy documents. We have proposed in a previous work a robust separation system relying on a proximity string segmentation algorithm. The extracted pseudo-lines and pseudo-words are used as basic blocks for classification. A multi-class support vector machine (SVM) with Gaussian kernel associates first an appropriate label to each pseudo-word. Then, the local neighborhood of each pseudo-word is studied in order to propagate the context and correct the classification errors. In this work, we first propose to model the separation problem by conditional random fields considering the horizontal neighborhood. As the considered neighborhood is too local to solve certain error cases, we have enhanced this method by using a more global context based on class dominance in the pseudo-line. The method has been evaluated on business documents. It separates handwritten and printed text with better scores (99.1% and 99.2% respectively), contrary to noise which is very random in these documents (90.1%)

Un classifieur hybride pour la reconnaissance d'expressions mathématiques manuscrites en-ligne

National audienc