Text Region Conditional Generative Adversarial Network for Text Concealment in the Wild

Textual information appearing on the captured image may contain personal information. In various circumstances, publishing such images in the public domain may create a threat of privacy leak. To avoid these situations, we propose a text concealment method. To accomplish this task, we have used a conditional generator, which is a text region conditioned concealment network. The text regions predicted by the detector network are used as a conditioning criterion for the concealment process. The text region prediction in the form of a word-level bounding box may contain stroke pixels as well as the background pixels. However, to reduce the number of background pixels for proper conditioning of text concealment network, a character level annotation is used for the generator in place of word-level bounding box annotation. It helps to focus more on strokes as compared to the background pixels. A character-level symmetric line representation of text has been proposed to obtain finer level text region prediction as compared to the character-level bounding box. The proposed model is trainable from end-to-end. The text region conditioned generator is trained from the loss of global and local discriminators. The proposed method is validated on public scene text image datasets such as ICDAR 2015, COCO-Text and Synthesis dataset. The proposed architecture shows competitive results as compared to other state-of-the-art approaches.