Éditeur(s) : HAL CCSD
Résumé : International audience
We propose in this paper a new online Multi-UserMulti-Touch handwritten diagram DataBase (MUMTDB) forevaluating recognition systems under the multi-user situation.The data is collected according to two predefined mind mapscenarios which contains 9 classes of graphical symbols. Eachscenario is completed by involving two users at the same time.Since the users are given freedom to draw the symbols asthey want, the dataset contains a diversity of multi-stroke andeven multi-touch symbols. It allows addressing new challengingproblems regarding the recognition of simultaneous composition of structured documents. The dataset is freely availableon-line.
International Conference on Frontiers in Handwriting Recognition (ICFHR)
Shenzhen, China

hal-01374376
https://hal.univ-antilles.fr/hal-01374376

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we present a novel method for recognition of handwritten sketches. Unlike previous approaches, we focus on online retrieval and ability to build our model incrementally, thus we do not need to know all the data in advance and we can achieve very good recognition results after as few as 15 samples. The method is composed of two main parts: feature representation and learning and recognition. In feature representation part, we utilize SIFT-like feature descriptors in combination with soft response Bag-of-Words techniques. Descriptors are extracted locally using our novel sketch-specific sampling strategy and for support regions we follow patch-based approach. For learning and recognition, we use a novel technique based on fuzzy-neural networks, which has shown good performance in incremental learning. The experiments on state-of-the-art benchmarks have shown promising results.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01166497
https://hal.univ-antilles.fr/hal-01166497
https://hal.univ-antilles.fr/hal-01166497/document
https://hal.univ-antilles.fr/hal-01166497/file/IGS_2015_submission_37.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
The large majority of methods proposed in literature for handwriting recognition assume that words are produced drawing large parts of the ink without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous handwriting, minimizing the number of pen-up during the production of a word, others do not. As a consequence, whenever the handwriting presents pen-up within a word, the recognition performance can drop significantly. In a preliminary study, we presented an algorithm for discriminating among different types of ink appearing in handwriting, namely isolated characters, cursive, dots, horizontal and vertical bars, based on the use of a suitable set of features. In this paper, we have characterized the discriminative power of each considered feature according to different measures and we have proposed a method for combining the different feature rankings. We have also used the Fischer's Linear Discriminant Analysis (LDA) for exhaustively selecting the best feature subsets with increasing number of features. Finally, we have compared the results obtained by using the feature subsets provided by LDA with those obtained with the feature subsets selected according to our feature ranking. The experimental results, on different datasets of handwritten words, showed that our approach successfully achieves its aim allowing to reduce the computational cost without affecting the overall performance of the recognition process.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165877
https://hal.univ-antilles.fr/hal-01165877
https://hal.univ-antilles.fr/hal-01165877/document
https://hal.univ-antilles.fr/hal-01165877/file/IGS_2015_submission_13.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we introduce a framework for on-line learning of handwritten symbols from scratch. As such, learning suffers from missing data at the beginning of the learning process, in this paper we propose the use of Sigma-lognormal model to generate synthetic data. Our framework deals with a real-time use of the system, where the recognition of a single symbol cannot be postponed by the generation of synthetic data. We evaluate the use of our framework and Sigma-lognormal model by comparison of the recognition rate to a block-learning and learning without any synthetic data. Experimental results show that both of these contributions represent an enhancement to the on-line handwriting recognition, especially when starting from scratch.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165923
https://hal.univ-antilles.fr/hal-01165923
https://hal.univ-antilles.fr/hal-01165923/document
https://hal.univ-antilles.fr/hal-01165923/file/IGS_2015_submission_34.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
Drawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

Droits : http://creativecommons.org/licenses/by-nc-nd/
ISBN : 979-10-95177-01-2
hal-01166509
https://hal.univ-antilles.fr/hal-01166509
https://hal.univ-antilles.fr/hal-01166509/document
https://hal.univ-antilles.fr/hal-01166509/file/IGS2015proceedingsvHal-1.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165890
https://hal.univ-antilles.fr/hal-01165890
https://hal.univ-antilles.fr/hal-01165890/document
https://hal.univ-antilles.fr/hal-01165890/file/IGS_2015_submission_20.pdf

Éditeur(s) : HAL CCSD
Résumé : Best paper for innovative research
International audience
This paper investigates the interest of using the Kinematic Theory of rapid human movements to analyseand evaluate the handwriting produced by young kindergarten children in typical classroom environment. A total of66 children participated in this trial. For the preliminary results reported herein, movements from 15 children, takenevenly from three different levels (3, 4, and 5 years old), were analyzed. Our results confirm that 1) the sigmalognormalequation can model accurately childen’s movement and 2) that this modeling can differentiate betweenchildren of different school levels, hence supporting the relevance of pursuing along this original pathway.
Recent Progress in Graphonomics: Learn from the Past. IGS 2013
Nara, Japan

hal-01098125
https://hal.archives-ouvertes.fr/hal-01098125