Abstract: A collaboration database service may allow users to create, edit, and share collaborative content on custom databases, and may also integrate and interoperate with various productivity applications. For example, a collaboration database engine of the service may detect creation or modification of a table through an application associated with the service. In response to a request to add a choice type column to the table, the engine may automatically select, provide, and enable modification of a set of symbols and associated text values corresponding to choices. The engine may then provide the choice type column for display within the table such that each cell of the choice type column is automatically and/or manually populated with at least one of the choices. The choice may be represented by a symbol and/or associated text value from the modified set of symbols and associated text values corresponding to the choice.

Abstract: A collaboration database service may allow users to create, edit, and share collaborative content on custom databases, and may also integrate and interoperate with various productivity applications. For example, a collaboration database engine of the service may detect creation or modification of a table through an application associated with the service. In response to a request to add a choice type column to the table, the engine may automatically select, provide, and enable modification of a set of symbols and associated text values corresponding to choices. The engine may then provide the choice type column for display within the table such that each cell of the choice type column is automatically and/or manually populated with at least one of the choices. The choice may be represented by a symbol and/or associated text value from the modified set of symbols and associated text values corresponding to the choice.

Abstract: Various technologies and techniques are disclosed that improve handwriting recognition operations. A balancing factor is calculated that can be used in recognition mode to compare out-of-dictionary recognition scores with in-dictionary recognition scores. Correct ink samples of words are provided, with some in-dictionary and some out-of-dictionary words. One or more tuning sets are generated from the samples, the tuning set(s) having a percentage of out-of-dictionary words based upon what is expected for a typical user. A handwriting recognizer is run against the tuning set(s) to determine statistics. The statistics are based upon the recognized answer for each word compared to the correct answer. The statistics are used to calculate the balancing factor. During recognition mode, a user inputs handwriting to be recognized. The balancing factor is used to compare and combine an in-dictionary alternate list with an out-of-dictionary alternate list to reach a recognition decision.

Abstract: Various technologies and techniques are disclosed that improve handwriting recognition operations. A balancing factor is calculated that can be used in recognition mode to compare out-of-dictionary recognition scores with in-dictionary recognition scores. Correct ink samples of words are provided, with some in-dictionary and some out-of-dictionary words. One or more tuning sets are generated from the samples, the tuning set(s) having a percentage of out-of-dictionary words based upon what is expected for a typical user. A handwriting recognizer is run against the tuning set(s) to determine statistics. The statistics are based upon the recognized answer for each word compared to the correct answer. The statistics are used to calculate the balancing factor. During recognition mode, a user inputs handwriting to be recognized. The balancing factor is used to compare and combine an in-dictionary alternate list with an out-of-dictionary alternate list to reach a recognition decision.

Abstract: Various technologies and techniques are disclosed that improve handwriting recognition accuracy. A set of character classes that are suitable across the various languages to be supported is established. The characters in one or more of the languages to be supported are grouped into the character classes. Probabilities are determined for the character classes. The character classes and the character class probabilities are used in a language-independent language model. The language-independent language model is then used to improve handwriting recognition operations when ambiguous handwriting is input by a user. The recognized characters are displayed to the user after the ambiguity is resolved.

Abstract: A technique for reducing a prototype database for use with handwriting recognizers. The prototype database is divided into multiple spaces and the effect on the error rate for removing each prototype is identified. All prototypes that contribute to the error rate are removed. If further prototype database reduction is desired, the technique identifies and removes one or more prototypes that have the least adverse effect on the error rate across all spaces. The technique continues removing prototypes in this manner until a desired prototype database size has been achieved.

Abstract: A technique for reducing a prototype database for use with handwriting recognizers. The prototype database is divided into multiple spaces and the effect on the error rate for removing each prototype is identified. All prototypes that contribute to the error rate are removed. If further prototype database reduction is desired, the technique identifies and removes one or more prototypes that have the least adverse effect on the error rate across all spaces. The technique continues removing prototypes in this manner until a desired prototype database size has been achieved.