Pattern recognition employing arbitrary segmentation and compound probabilistic evaluation

A method for classifying an image of a word, comprising the steps of:

• dividing the image into a plurality of segments of arbitrary size;
• combining the segments to form a plurality of different pattern sequences, wherein each pattern sequence contains a string of possible characters, and wherein different pattern sequences contain a different number of possible characters;
• determining a probability for each character in a given string of possible characters, and multiplying the probabilities for each character in the string to determine a composite probability for the corresponding pattern sequence;
• adjusting the composite probability by a normalization factor that is related to the number of characters in the string;
• and
• selecting one of the pattern sequences on the basis of its adjusted composite probability.

A method for classifying an image of a word comprising the steps of:

• dividing the image into a plurality of sufficiently small segments, wherein no one segment overlaps more than a recognizable pixel pattern;
• combining the plurality of segments to form a plurality of possible character sequences, wherein different character sequences have a different number of characters;
• selecting a number of character sequences from amongst said plurality of possible character sequences, based on the contextual probability associated with less than all of the characters in each of the plurality of possible character sequences;
• and
• determining a classification probability value for each of the selected character sequences using dynamic programming techniques, wherein the character sequences are selected on the basis of a limited number of characters in the character sequence.

A method for classifying an image comprised of a plurality of features, said method comprising the steps of:

• segmenting the image into a plurality of pieces of arbitrary size;
• combining said pieces into a plurality of pattern sequences, each representing a different combination of the pieces;
• determining a segmentation probability for each pattern sequence, wherein said segmentation probability represents a likelihood that the image has been correctly segmented and the pieces have been correctly combined;
• and
• determining a most likely pattern sequence based upon said segmentation probability.