Removing Embedded Text in Images via Fully Convolutional Networks with Generative Adversarial Learning

• Main Authors: Shu-Heng Chen, 陳書恆
• Other Authors: 曾定章
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/f68377

碩士 === 國立中央大學 === 資訊工程學系 === 105 === An image embedded by texts is one of the most common 2D media in the web; for example, the netizen produce lots of this kind pictures or memes for different purposes. In some situations, the added texts make a beauty picture into a garbage. For example, we cannot use the image for some other purposes, such as scene recognition, object classification, …, etc. Therefore, in this study, we aim to propose a system that can clean texts automatically on a given image and inpaint or restore the image. With novel generation of computer technology, the deep learning architecture can be applied on the inpainting problem and perform better results than several traditional methods. In the proposed system, we construct two modules using the latest and novel deep learning frameworks to get a great result. The first module, mask generation module, is used for detecting the embedded texts in a given image automatically and products the corresponding bitmap image mask. The second module, image completion module, can inpaint the corrupt images based on the given mask image. In the experiments, we compare our results with two fully developed and without deep learning technique methods. We show that the proposed method can provide more natural and less flawed results than the classic image inpainting methods provided.