A computational theory of reading and an algorithmic realization of the theory is presented that illustrates the application of the methodology of a computational theory to an engineering problem. The theory is based on past studies of how people read that show there are two steps of visual processing in reading and that these steps are influenced by cognitive processes. This paper discusses the development of a similar set of algorithms. A gross visual description of a word is used to suggest a set of hypotheses about its identity. These then drive further selective analysis of the image that can be altered by knowledge of language characteristics such as syntax. This is not a character recognition algorithm since an explicit segmentation of a word and a recognition of its isolated characters is avoided. This paper presents a unified discussion of this methodology with a concentration on the second stage of selective image analysis. An algorithm is presented that determines the minimum number of tests that have to be programmed under the constraint that the minimum number of tests are to be executed. This is used to compare the proposed technique to a similar character recognition algorithm.