Most bi-level images stored on computers today comprise scanned text, and their number is escalating because of the drive to archive large volumes of paper-based material electronically. These documents 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. Such techniques are called document image compression methods.