Nowadays, with the development of electronic devices, more and more attention has been paid to camera-based text processing. Different from scene image, the recognition system of document image needs to sort out the recognition results and store them in the structured document for the subsequent data processing. However, in document images, the fusion of text lines largely depends on their semantic information rather than just the distance between the characters, which causes the problem of learning confusion in training. At the same time, for multi-directional printed characters in document images, it is necessary to use additional directional information to guide subsequent recognition tasks. In order to avoid learning confusion and get recognition-friendly detection results, we propose a character-level text detection framework, DetectGAN, based on the conditional generative adversarial networks (abbreviation cGAN used in the text). In the proposed framework, position regression and NMS process are removed, and the problem of text detection is directly transformed into an image-to-image generation problem. Experimental results show that our method has an excellent effect on text detection of camera-captured document images and outperforms the classical and state-of-the-art algorithms.