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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

On the Fusion of Text Detection Results: A Genetic Programming Approach

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Author(s):
Campana, Jose L. Flores [1] ; Pinto, Allan [1] ; Cordova Neira, Manuel Alberto [1] ; Lorgus Decker, Luis Gustavo [1] ; Santos, Andreza [1] ; Conceicao, Jhonatas S. [1] ; Torres, Ricardo da Silva [2]
Total Authors: 7
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] Norwegian Univ Sci & Technol NTNU, Fac Informat Technol & Elect Engn, Dept ICT & Nat Sci, N-6009 Alesund - Norway
Total Affiliations: 2
Document type: Journal article
Source: IEEE ACCESS; v. 8, p. 81257-81270, 2020.
Web of Science Citations: 0
Abstract

Hundreds of text detection methods have been proposed, motivated by their widespread use in several applications. Despite the huge progress in the area, which includes even the use of sophisticated learning schemes, ad-hoc post-processing procedures are often employed to improve the text detection rate, by removing both false positives and negatives. Another issue refers to the lack of the use of the complementary views provided by different text detection methods. This paper aims to fill these gaps. We propose the use of a soft computing framework, based on genetic programming (GP), to guide the definition of suitable post-processing procedures through the combination of basic operators, which may be applied to improve detection results provided by multiple methods at the same time. Performed experiments in the widely used ICDAR 2011, ICDAR 2013, and ICDAR 2015 datasets demonstrate that our GP-based approach leads to F1 effectiveness gains up to 5.1 percentage points, when compared to several baselines. (AU)

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