Word spotting in bitmap images using context-sensitive character models without baselines

For use in a keyword-spotting network that includes at least one keyword hidden Markov model (HMM), a method of providing a context-sensitive, character-based, non-keyword HMM comprising:

• providing first, second, third, and fourth sets of character HMMs, each set modeling a set of characters in a particular context;
• constructing respective single-context character set HMMs, each having a respective set of character HMMs for the respective context connected in parallel between null states with a return loop;
• and
• connecting the single-context character set HMMs in parallel between null states with no return loops whereby the optimal path for a non-keyword through the HMM is constrained by the context of the characters in the non-keyword.

For use in a keyword-spotting network that includes at least one keyword hidden Markov model (HMM), a method of providing a context-sensitive, image-slice based, non-keyword HMM comprising:

• providing first, second, third, and fourth sets of image-slice states corresponding to first, second, third, and fourth contexts, wherein the image-slice states for a given set model vertical slices of character images in the particular context for that set;
• constructing respective single-context image-slice set HMMs, each having the image-slice states for the respective context connected in parallel between null states with a return loop;
• and
• connecting the single-context image-slice set HMMs in parallel between null states with no return loops whereby the optimal path for a non-keyword through the HMM is constrained by the context of the characters in the non-keyword.

For use in a processor-based method of determining whether a keyword is present in a bitmap input image wherein the input image is subjected to an operation that determines bounding boxes for portions of the input image that potentially contain text and an operation that generates a sequence of features for each such portion of the input image, the text being considered to extend horizontally, a method for modeling the appearance of keywords in the input image, the method comprising the steps of:

• providing a set of previously-trained single-character hidden Markov models (HMMs);
• concatenating those single-character HMMs that correspond to the characters in the keyword so as to define a keyword HMM;
• and
• constructing an HMM network that includes the keyword HMM;
• said providing step being characterized in that:
• each character has a shape that is characterized by a number of feature vectors at a corresponding number of 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 a respective one of the number of distinct portions of the given character,
• each character has a number of distinct portions, the number being greater than 1 for at least some characters,
• each distinct portion spans a respective contiguous subset of the plurality of horizontal locations,
• each state is characterized by a statistical distribution of the feature vectors that characterize the corresponding distinct portion of the given character,
• each single-character HMM has a number of possible contexts depending on whether the character has an ascender or descender,
• the single-character HMMS that are concatenated to form the keyword HMM have the same context, and
• the feature vector for a given horizontal location in a given character includes a representation of an upper and lower character boundary referenced to a bounding box for the keyword.