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.
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. 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.