# Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN
## 1 Introduction  
In the last decade, scene text has been extensively studied. Since the development of convolutional neural networks, many solutions have been proposed. Scene text segmentation focuses on precisely separating the text from the background and outlining the character blocks. Therefore, text segmentation is an effective means to obtain text information in images, and it is a research hotspot in the computer field.  
The difficulty of this work is that the font, color, and shape of the scene text are very different. The early methods based on image processing are mainly threshold algorithms [1; 2; 3; 4; 5; 6], but they are very sensitive to the selection of threshold values in complex backgrounds, and the adaptability of different scenes is limited. Later, the proposed method combining text segmentation with probabilistic models [7; 8; 9; 10] faced challenges such as data dependence and parameter estimation. Clustering methods [11; 12; 13] are also used to deal with text segmentation. With the development of neural networks, convolutional network-based methods can effectively handle various cases. However, the bottleneck of this approach is the high dependence on large-scale data, especially manually labeled data. For this reason, unsupervised segmentation networks have been proposed, such as fully convolutional adaptive networks (FCANs) [14], CyCADA [15] and CrDoCo [16]. However, these methods deal with the segmentation of street scenes, which is not helpful for the segmentation of text.  
To this end, we propose using an unsupervised method to accomplish text segmentation with a complex background. We propose treating the segmentation task of scene text as a kind of style transfer of image, which is a novel perspective. We choose a CycleGANnetwork [17] that can carry out unsupervised style transfer. The two GAN networks are constrained by each other to form a cyclic adversarial network. A scene text image with a complex background is transformed into a text image with a simple background, and the model does not need to consider the interference of the complex background in the image. CycleGAN, on the other hand, learns network mapping without supervision. Scene image text segmentation is relatively simple in terms of semantic content, but the details of text strokes are more important. Therefore, in order to improve the edge detail of the generated image, we introduce the ASPP module [18]. When acquiring image features, ASPP can expand the receptive field, extract more abundant global information, improve the edge information of CycleGAN when generating segmentation images, and reduce the edge noise. This paper makes the following contributions:  
* This paper regards scene image text segmentation as a style transfer problem between images. From a scene text image with a complex background, after processing, it is transformed into a text image with a concise background, which provides convenience for subsequent processing. This transformation process actually reveals a novel perspective.
* This paper uses CycleGAN for the text segmentation of scene images. The detail processing of the edge of the text segmentation image generated by CycleGAN is not enough and not clear enough. The ASPP module is introduced into CycleGAN's generator to obtain multi-scale text features and retain the details generated by image text edges.
* This paper conducts experiments on synthetic data sets, IIIT 5k word data sets, MATC data sets and MTWI data sets. Experimental results show that our method can effectively segment text while retaining clear edge information.