A substantial portion of research and applications for visual image recognition have been limited to the recognition of large, isolated, non-variant images. Performing a visual search for focusing on, locating, and recognizing smaller details within the context of a larger image has proven more difficult. This paper presents a system that is capable of learning words of various lengths, and then locating and recognizing a previously trained word within a noisy document. This system utilizes a fitness function, search routine and viewing window to identify possible word candidates, and then employs the Hausdorff-Voronoi network (HAVNET) for word recognition. After 330 searches of 30 different words, with document noise ranging from 0 - 20%, the system recognition and location accuracy were 97.3%.