Language-independent language model using character classes

1 . A method for improving handwriting recognition comprising the steps of:

establishing a plurality of character classes to use that are suitable across a plurality of languages to be supported;

analyzing a plurality of characters in at least one of the plurality of languages to be supported and grouping the plurality of characters into the character classes;

determining probabilities for the character classes; and

using at least a portion of the character class probabilities to improve a handwriting recognition operation from handwritten input received from a user.

2 . The method of claim 1 , wherein the probabilities are bigram probabilities.

3 . The method of claim 1 , wherein the probabilities are unigram probabilities.

4 . The method of claim 1 , wherein the probabilities are trigram probabilities.

5 . The method of claim 1 , wherein the using step includes calculating a new recognition score by multiplying a score of a character recognition by a character class probability score determined using the at least a portion of the character class probabilities, and wherein the new recognition score is used to improve the handwriting recognition operation.

6 . The method of claim 5 , wherein the handwriting recognition operation is improved by using the new score to resolve an ambiguity.

7 . The method of claim 1 , wherein the character classes are selected from the group consisting of white space, digits, upper case, lower case, trailing punctuation, leading punctuation, and symbols.

8 . The method of claim 1 , wherein the character class probabilities are bigram class transition probabilities, and wherein the bigram class transition probabilities are used to improve the handwriting recognition operation by determining which character class transition is more likely to occur.

9 . The method of claim 1 , wherein the character class probabilities are generated according to a process selected from the group consisting of using a set of samples, using a manual operation, and using an ad-hoc operation.

10 . A computer-readable medium having computer-executable instructions for causing a computer to perform the steps recited in claim 1 .

11 . A computer-readable medium having computer-executable instructions for causing a computer to perform steps comprising:

establish a plurality of character classes to use that are suitable across a plurality of languages to be supported;

analyze a plurality of characters in at least one of the languages to be supported and group the characters into the character classes;

determine a plurality of character class probabilities;

determine that an ambiguity exists in a handwritten input received from a user; and

use at least a portion of the character class probabilities to resolve the ambiguity.

12 . The computer-readable medium of claim 11 , wherein the character class probabilities are selected from the group consisting of bigram probabilities, unigram probabilities, and trigram probabilities.

13 . The computer-readable medium of claim 11 , wherein the character class probabilities are bigram class transition probabilities, and wherein the bigram class transition probabilities are used to resolve the ambiguity by determining which character class transition is more likely to occur.

14 . A method for improving handwriting recognition using a language-independent language model comprising the steps of:

generating a language-independent language model that includes a plurality of character classes and a plurality of character class probabilities;

receiving handwritten input from a user;

determining that the handwritten input is ambiguous;

using at least a portion of the character class probabilities to help resolve the ambiguity; and

displaying the recognized characters.

15 . The method of claim 14 , wherein the character class probabilities are generated using a first language, and wherein the handwritten input from the user is in a second language.

16 . The method of claim 14 , wherein the character class probabilities include bigram probabilities.

17 . The method of claim 14 , wherein the character class probabilities include trigram probabilities.

18 . The method of claim 14 , wherein the character class probabilities include unigram probabilities.

19 . The method of claim 14 , wherein the character classes are suitable across a plurality of languages to be supported by the language model.

20 . A computer-readable medium having computer-executable instructions for causing a computer to perform the steps recited in claim 14 .