Extraction and Classification of Handwritten Annotations

This article describes a method for extracting and classifying handwritten annotations on printed documents using a simple camera integrated in a lamp or a mobile phone. The ambition of such a research is to offer a seamless integration of notes taken on printed paper in our daily interactions with digital documents. Existing studies propose a classification of annotations based on their form and function. We demonstrate a method for automating such a classification and report experimental results showing the classification accuracy

Cognitive and social effects of handwritten annotations

This article first describes a method for extracting and classifying handwritten annotations on printed documents using a simple camera integrated in a lamp. The ambition of such a research is to offer a seamless integration of notes taken on printed paper in our daily interactions with digital documents. Existing studies propose a classification of annotations based on their form and function. We demonstrate a method for automating such a classification and report experimental results showing the classification accuracy. In the second part of the article we provide a road map for conducting user-centered studies using eye-tracking systems aiming to investigate the cognitive roles and social effects of annotations. Based on our understanding of some research questions arising from this experiment, in the last part of the article we describe a social learning environment that facilitates knowledge sharing across a class of students or a group of colleagues through shared annotations

Boosting bonsai trees for handwritten/printed text discrimination

International audienceBoosting over decision-stumps proved its e ciency in Natural Language Processing essentially with symbolic features, and its good properties (fast, few and not critical parameters, not sensitive to overfitting) could be of great interest in the numeric world of pixel images. In this article we investigated the use of boosting over small decision trees, in image classification processing, for the discrimination of handwritten/printed text. Then, we conducted experiments to compare it to usual SVM-based classification revealing convincing results with very close performance, but with faster predictions and behaving far less as a black-box. Those promising results tend to make use of this classifier in more complex recognition tasks like multiclass problems

Contributions au tri automatique de documents et de courrier d'entreprises

Ce travail de thèse s inscrit dans le cadre du développement de systèmes de vision industrielle pour le tri automatique de documents et de courriers d entreprises. Les architectures existantes, dont nous avons balayé les spécificités dans les trois premiers chapitres de la thèse, présentent des faiblesses qui se traduisent par des erreurs de lecture et des rejets que l on impute encore trop souvent aux OCR. Or, les étapes responsables de ces rejets et de ces erreurs de lecture sont les premières à intervenir dans le processus. Nous avons ainsi choisi de porter notre contribution sur les aspects inhérents à la segmentation des images de courriers et la localisation de leurs régions d intérêt en investissant une nouvelle approche pyramidale de modélisation par coloration hiérarchique de graphes ; à ce jour, la coloration de graphes n a jamais été exploitée dans un tel contexte. Elle intervient dans notre contribution à toutes les étapes d analyse de la structure des documents ainsi que dans la prise de décision pour la reconnaissance (reconnaissance de la nature du document à traiter et reconnaissance du bloc adresse). Notre architecture a été conçue pour réaliser essentiellement les étapes d analyse de structures et de reconnaissance en garantissant une réelle coopération entres les différents modules d analyse et de décision. Elle s articule autour de trois grandes parties : une partie de segmentation bas niveau (binarisation et recherche de connexités), une partie d extraction de la structure physique par coloration hiérarchique de graphe et une partie de localisation de blocs adresse et de classification de documents. Les algorithmes impliqués dans le système ont été conçus pour leur rapidité d exécution (en adéquation avec les contraintes de temps réels), leur robustesse, et leur compatibilité. Les expérimentations réalisées dans ce contexte sont très encourageantes et offrent également de nouvelles perspectives à une plus grande diversité d images de documents.This thesis deals with the development of industrial vision systems for automatic business documents and mail sorting. These systems need very high processing time, accuracy and precision of results. The current systems are most of time made of sequential modules needing fast and efficient algorithms throughout the processing line: from low to high level stages of analysis and content recognition. The existing architectures that we have described in the three first chapters of the thesis have shown their weaknesses that are expressed by reading errors and OCR rejections. The modules that are responsible of these rejections and reading errors are mostly the first to occur in the processes of image segmentation and interest regions location. Indeed, theses two processes, involving each other, are fundamental for the system performances and the efficiency of the automatic sorting lines. In this thesis, we have chosen to focus on different sides of mail images segmentation and of relevant zones (as address block) location. We have chosen to develop a model based on a new pyramidal approach using a hierarchical graph coloring. As for now, graph coloring has never been exploited in such context. It has been introduced in our contribution at every stage of document layout analysis for the recognition and decision tasks (kind of document or address block recognition). The recognition stage is made about a training process with a unique model of graph b-coloring. Our architecture is basically designed to guarantee a good cooperation bewtween the different modules of decision and analysis for the layout analysis and the recognition stages. It is composed of three main sections: the low-level segmentation (binarisation and connected component labeling), the physical layout extraction by hierarchical graph coloring and the address block location and document sorting. The algorithms involved in the system have been designed for their execution speed (matching with real time constraints), their robustness, and their compatibility. The experimentations made in this context are very encouraging and lead to investigate a wider diversity of document images.VILLEURBANNE-DOC'INSA-Bib. elec. (692669901) / SudocSudocFranceF