12. 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 HMMs, 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 provide 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 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 containing a word; providing an array of pixel values, referred to as a potential keyword, representing the portion of the input image; generating a set of features based on the potential keyword, the set of features providing shape information regarding the word potentially contained in the portion of the input image; the set of features being generated at a plurality of uniformly spaced horizontal locations, thereby avoiding segmentation of the potential keyword in a manner that depends on values of the pixels in the potential keyword; applying the set of features to the HMM network; finding a path through the network that maximizes the probability of the set of features as applied to the network; and determining whether the path that maximizes the probability passes through the keyword HMM so as to provide an indication whether the potential keyword is the keyword.
12. 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 HMMs, 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 provide 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 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 containing a word; providing an array of pixel values, referred to as a potential keyword, representing the portion of the input image; generating a set of features based on the potential keyword, the set of features providing shape information regarding the word potentially contained in the portion of the input image; the set of features being generated at a plurality of uniformly spaced horizontal locations, thereby avoiding segmentation of the potential keyword in a manner that depends on values of the pixels in the potential keyword; applying the set of features to the HMM network; finding a path through the network that maximizes the probability of the set of features as applied to the network; and determining whether the path that maximizes the probability passes through the keyword HMM so as to provide an indication whether the potential keyword is the keyword. 14. The method of claim 12 wherein the step of locating includes performing at least one reduction operation on the input image.