Authors:
Jun Yang
1
;
Zhaogong Zhang
1
and
Xuexia Wang
2
Affiliations:
1
School of Computer Science and Technology, Heilongjiang University, Harbin, China
;
2
Department of Mathematics, University of North Texas, Denton, U.S.A.
Keyword(s):
Deep Learning, Pattern Recognition, Curved Text Detection, Generative Adversarial Networks, Pixel Fluctuations Numbers.
Abstract:
Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, curved text detection is still a difficult problem that has not been addressed sufficiently. Presently, the most advanced method is based on segmentation to detect curved text. However, most segmentation algorithms based on convolutional neural networks have the problem of inaccurate segmentation results. In order to improve the effect of image segmentation, we propose a semantic segmentation network model based on generative adversarial networks and pixel fluctuations, denoted as GAPF; which is able to effectively improve the accuracy of text segmentation. The model consists of two parts: the generative model and the discriminative model. The main function of the generative model is to generate semantic segmentation graph, and then the discriminative model and generative model perform adversarial learning, which optimize the generative model to make the
generated image closer to the ground truth. In this paper, the information about pixel fluctuations numbers is input into the generative network as the segmentation condition to enhance the invariance of translation and rotation. Finally, a text boundary generation algorithm for text is designed, and the final detection result is obtained from the segmentation result. Experimental results on CTW1500, Total-Text, ICDAR 2015 and MSRA-TD500 demonstrate the effectiveness of our work.
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