R
Robert Sabourin
Researcher at École de technologie supérieure
Publications - 467
Citations - 12894
Robert Sabourin is an academic researcher from École de technologie supérieure. The author has contributed to research in topics: Handwriting recognition & Hidden Markov model. The author has an hindex of 53, co-authored 461 publications receiving 11391 citations. Previous affiliations of Robert Sabourin include Université du Québec & Pontifícia Universidade Católica do Paraná.
Papers
More filters
Journal ArticleDOI
From dynamic classifier selection to dynamic ensemble selection
TL;DR: This work proposes four new dynamic selection schemes which explore the properties of the oracle concept and suggests that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method.
Journal ArticleDOI
Dynamic classifier selection
TL;DR: An updated taxonomy of Dynamic Selection techniques is proposed based on the main characteristics found in a dynamic selection system, and an extensive experimental analysis, considering a total of 18 state-of-the-art dynamic selection techniques, as well as static ensemble combination and single classification models.
Journal ArticleDOI
Dynamic selection of classifiers-A comprehensive review
TL;DR: This comprehensive study observed that, for some classification problems, the performance contribution of the dynamic selection approach is statistically significant when compared to that of a single-based classifier and found evidence of a relation between the observed performance contribution and the complexity of the classification problem.
Journal ArticleDOI
Learning features for offline handwritten signature verification using deep convolutional neural networks
TL;DR: A novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user is proposed.
Journal ArticleDOI
An HMM-based approach for off-line unconstrained handwritten word modeling and recognition
TL;DR: A hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies and can be successfully used for handwritten word recognition.