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

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. INDEX TERMS Scene text detection, multi-oriented text, convolutional neural network, data fusion, genetic programming.

Scene text localization using lightweight convolutional networks

“…Docker provides a standardized unit software for packaging the source code and all its dependencies, including system tools, system libraries, and settings. Also, this work contributed to other publications [67,68,69]. Appendix A comprehends a copyright disclaimer for partial use of indexed articles on dissertationsor thesis.…”