This paper proposes a document segmentation, classification and recognition system for automatically reading daily-received office documents that have complex layout structures, such as multiple columns and mixed-mode contents of texts, graphics and half-one pictures. First, the block segmentation employs a two-step run-length smoothing algorithm for decomposing any document into single-mode blocks. Next, based on clustering rules the block classification classifies each block into one of text, horizontal or vertical lines, graphics, and pictures. The text block is separated into isolated characters using projection profiles, and which are translated into ASCII codes through a font- and size-independent character recognition subsystem. Logo pictures discriminated from half-tone pictures are identified and converted into symbolic words. The experimental results show that the proposed system is capable of correctly reading different styles of mixed-mode printed documents.