We propose a procedure that can improve the coding efficiency of bilevel image compression schemes that are based on chain coding techniques and entropy coders. The two chain coding techniques analyzed in this study employ three and eight symbols, respectively, to code the outline of object shapes in bilevel images. Once we have the chain code representation of a contour, we group the original chain symbols in blocks of two or more symbols to build new extended alphabets from which we can obtain more efficient compression rates by using a statistical model that best represents our new alphabets and entropy coding. The coding efficiency of every new alphabet is measured by its entropy, average length in bits per original source symbol, and the total length in bits that represent the compressed image. We compare the compression efficiency between our two chain coding techniques using our three entropy coding parameters for symbol groupings of different sizes. In our experiments, we also show how our blocking approach produces an entropy value that is lower than its expected theoretical counterpart for a given order of symbol groupings and, as a consequence, the average and total lengths are expected to follow a decreasing pattern as well. Finally, we compare our best encoding model against the Joint Bilevel Image Experts Group's JBIG and JBIG2 bilevel image compression standards.