caa a22 4500 445379510 CHVBK 20180317143013.0 cr unu---uuuuu 170323e20110301xx s 000 0 eng 10.1007/s10032-010-0125-5 doi (NATIONALLICENCE)springer-10.1007/s10032-010-0125-5 A protocol to characterize the descriptive power and the complementarity of shape descriptors [Elektronische Daten] [Muriel Visani, Oriol Terrades, Salvatore Tabbone] Most document analysis applications rely on the extraction of shape descriptors, which may be grouped into different categories, each category having its own advantages and drawbacks (O.R. Terrades etal. in Proceedings of ICDAR'07, pp. 227-231, 2007). In order to improve the richness of their description, many authors choose to combine multiple descriptors. Yet, most of the authors who propose a new descriptor content themselves with comparing its performance to the performance of a set of single state-of-the-art descriptors in a specific applicative context (e.g. symbol recognition, symbol spotting...). This results in a proliferation of the shape descriptors proposed in the literature. In this article, we propose an innovative protocol, the originality of which is to be as independent of the final application as possible and which relies on new quantitative and qualitative measures. We introduce two types of measures: while the measures of the first type are intended to characterize the descriptive power (in terms of uniqueness, distinctiveness and robustness towards noise) of a descriptor, the second type of measures characterizes the complementarity between multiple descriptors. Characterizing upstream the complementarity of shape descriptors is an alternative to the usual approach where the descriptors to be combined are selected by trial and error, considering the performance characteristics of the overall system. To illustrate the contribution of this protocol, we performed experimental studies using a set of descriptors and a set of symbols which are widely used by the community namely ART and SC descriptors and the GREC 2003 database. Springer-Verlag, 2010 Document analysis nationallicence Shape descriptors nationallicence Symbol description nationallicence Performance characterization nationallicence Complementarity analysis nationallicence Visani Muriel L3I, University of La Rochelle, La Rochelle Cedex 1, France aut Terrades Oriol Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Valencia, Spain aut Tabbone Salvatore LORIA, University of Nancy 2, Vandoeuvre-les-Nancy, France aut International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/1(2011-03-01), 87-100 1433-2833 14:1<87 2011 14 10032 https://doi.org/10.1007/s10032-010-0125-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-010-0125-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Visani Muriel L3I, University of La Rochelle, La Rochelle Cedex 1, France aut NATIONALLICENCE 700 1- Terrades Oriol Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Valencia, Spain aut NATIONALLICENCE 700 1- Tabbone Salvatore LORIA, University of Nancy 2, Vandoeuvre-les-Nancy, France aut NATIONALLICENCE 773 0- International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/1(2011-03-01), 87-100 1433-2833 14:1<87 2011 14 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer