Letter model and character bigram based language model for handwriting recognition

A computer system for recognizing user handwriting, the system comprising:

• an input device that receives input from a user and communicate the input as digital ink to an application;
• a word breaking component that divides the received digital ink into one or more words;
• an ink segmenting component that divides a word into one or more ink segments;
• a shape classifying component that determines one or more classifications that associate each ink segment as a part of a particular letter;
• a language model scoring component that assigns a probability to each of the classifications, the language model comprising bigram probabilities computed in advance from a corpora, each bigram probability indicating a probability that two letters occur in succession, the scoring component computing a score according to bigram probabilities corresponding to letters identified by the classifying component, each score further computed according to probabilities segments are successive parts of a same letter, each score further computed according to probabilities the segments are end segments of a letter identifying by the shape classifying component;
• a recognition result component configured to produce a recognition result for each word based on scores computed by the language model.

A computer-readable storage device encoded with instructions for controlling a computer system to determine a score for a recognition alternative, by a method comprising:

• receiving a digital ink segment that makes up part of a word;
• adjusting a score based on output of a shape classifier for a recognition alternative of the digital ink segment;
• if the digital ink segment is the last segment in a letter, adjusting the score based on the probability that the letter ends in the digital ink segment;
• if there are more ink segments in the word, adjusting the score based on a bigram probability between the letter and a subsequent letter, the bigram probability selected according to the letter and the subsequent letter from among a set of bigram probabilities computed in advance from a corpora, wherein each bigram probability indicating a probability that two letters appear in succession in a word;
• and
• if the digital ink segment is the last segment in the word, providing the score for the recognition alternative by applying a weak-constrained language model that recognizes words not present in a dictionary of words of a currently selected language, the weak-constrained language model comprising probabilities that segments are last segments of letters.