Most bi-level images stored on computers today comprise scanned text, and are stored using generic bi-level image technology based either on classical run-length coding, such as the CCITT Group 4 method, or on modern schemes such as JBIG that predict pixels from their local image context. However, image compression methods that are tailored specifically for images known to contain printed text can provide noticeably superior performance because they effectively enlarge the context to the character level, at least for those predictions for which such a context is relevant. To deal effectively with general documents that contain text and pictures, it is necessary to detect layout and structural information from the image, and employ different compression techniques for different parts of the image. The authors extend previous work in document image compression in two ways. First, we include automatic discrimination between text and non-text zones in an image. Second, the system is tested on a large real-world image corpus.