학술논문

A Fusion Strategy for the Single Shot Text Detector
Document Type
Conference
Source
2018 24th International Conference on Pattern Recognition (ICPR) Pattern Recognition (ICPR), 2018 24th International Conference on. :3687-3691 Aug, 2018
Subject
Computing and Processing
Signal Processing and Analysis
Detectors
Convolution
Neural networks
Feature extraction
Fuses
Aggregates
Training
Language
Abstract
In this paper, we propose a new fusion strategy for scene text detection. The system is based on a single fully convolution network, which outputs the coordinates of text bounding boxes at multiple scales. We improve the performance of text detection by combining a fusion strategy. This strategy obtains precise text bounding boxes according to the confidence of candidate text boxes. It exhibits promising robustness and discriminative power by fusing text boxes. Experimental results on ICDAR2011 and ICDAR2013 datasets indicate the effectiveness and robustness of the proposed fusion strategy with an F-measure of 87%, which outperforms the base network 2%.