31. The method of claim 24 wherein a feature vector for a given horizontal location includes a representation of the topmost pixel at the given horizontal location having a value above a threshold, a representation of the bottommost pixel at the given horizontal location having a value above the threshold, and a representation of pixel values between the topmost pixel having a value above the threshold and the bottommost pixel having a value above the threshold.
24. A processor-based method of determining whether a keyword made up of characters is present in a bitmap input image containing words, the words being considered to extend horizontally, the method comprising the steps of: providing a set of previously trained single-character hidden Markov models (HMMs) wherein each character on which one of the set of single-replacement HMMs is based has a number of distinct portions, each character has a shape that is characterized by feature vectors at corresponding horizontal locations along the character, a given single-character HMM for a given character is characterized by a number of states, each state of which corresponds to one of the number of distinct portions of the given character, and each state is characterized by a probability distribution of the feature vectors that characterize the corresponding distinct portion of the given character; each single-character HMM having a number of possible contexts, depending on whether the character has an ascender or a descender; concatenating those single-character HMMs that correspond to the characters in the keyword so as to create a keyword HMM, the context of a given single-character HMM used to create the keyword HMM being determined on the basis of whether the keyword contains characters having ascenders or a descenders; providing a non-keyword HMM; constructing a network that includes a first path passing through the keyword HMM and a second path passing through the non-keyword HMM but not passing through the keyword HMM; locating a portion of the input image potentially representing a word; providing an array of pixel values, referred to as a potential keyword, representing the portion of the input image, the potential keyword having a plurality of vertically extending columns of pixels at respective ones of a plurality of uniformly spaced horizontal locations; generating a plurality of feature vectors determined at respective ones of the plurality of horizontal locations in the potential keyword, a given feature vector for a given horizontal location being specified by pixel values in the column at the given horizontal location; the plurality of feature vectors together representing word shape while being generated by uniform segmentation of the potential keyword, whereby segmentation of a type that depends on values of the pixels in the potential keyword is avoided; applying the plurality of feature vectors to the HMM network; finding a path through the network that maximizes the probability of the plurality of feature vectors as applied to the network; and determining whether the path that maximizes the probability passes through the keyboard HMM, so as to provide an indication whether the potential keyword is the keyword.