In this paper a genetic matching scheme is extended to take into account primitive arcs and complex description in the pattern recognition process. Classical ways only focus on segments and are sensitive to over segmentation effect. Our approach allows to improve the recognition by handling more accurate description and also to decrease processing time by limiting the number of vertices to match. Experimental studies using real data attest the robustness of our approach.
Jean-Pierre Salmon, Laurent Wendling. ARG based on arcs and segments to improve the symbol recognition by genetic algorithm. Seventh International Workshop on Graphics Recognition - GREC'2007, IAPR TC-10 (Technical Committee on Graphics Recognition), Sep 2007, Curitiba, Brazil. 11 p. ⟨inria-00184880⟩ - lien externe
Citations
APA
Salmon, J.-P., & Wendling, L. (2007). ARG based on arcs and segments to improve the symbol recognition by genetic algorithm. https://inria.hal.science/inria-00184880v1
MLA
Salmon, Jean-Pierre, and Laurent Wendling. ARG Based on Arcs and Segments to Improve the Symbol Recognition by Genetic Algorithm. Jan. 2007, https://inria.hal.science/inria-00184880v1.
Chicago
Salmon, Jean-Pierre, and Laurent Wendling. 2007. “ARG Based on Arcs and Segments to Improve the Symbol Recognition by Genetic Algorithm.” https://inria.hal.science/inria-00184880v1.
Harvard
Salmon, J.-P. and Wendling, L. (2007) “ARG based on arcs and segments to improve the symbol recognition by genetic algorithm.” Available at: https://inria.hal.science/inria-00184880v1.
ISO 690
SALMON, Jean-Pierre and WENDLING, Laurent, 2007. ARG based on arcs and segments to improve the symbol recognition by genetic algorithm [en ligne]. January 2007. Disponible à l'adresse : https://inria.hal.science/inria-00184880v1