Digital Ink Database Searching Using Handwriting Feature Synthesis

Abstract

A method of searching a digital ink database is disclosed. The digital ink database is associated with a specific author. The method starts by receiving a computer text query from an input device. The computer text query is then mapped to a set of feature vectors using a handwriting model of that specific author. As a result, the set of feature vectors approximates features that would have been extracted had that specific author written the computer query text by hand. Finally, the set of feature vectors is used to search the digital ink database.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]The present application is a Continuation application of U.S. application Ser. No. 10 / 492,169 filed on Apr. 9, 2004 which is a National Phase Application of PCT / AU02 / 0395, filed on Oct. 15, 2002, all of which is herein incorporated by reference.TECHNICAL FIELD[0002]The present invention broadly relates to digital processor implemented handwriting searching or recognition systems, and in particular, to a method of and apparatus for searching of a digital ink database using handwriting feature synthesis from a search query in text form.BACKGROUND ART[0003]“Digital ink database” as used herein refers to a database which stores handwritten characters, for example a string of handwritten characters forming a handwritten letter.Overview[0004]Pen-based computing systems provide a convenient and flexible means of human-computer interaction. Most people are very familiar with using pen and paper. This familiarity is exploited by known systems which...

AI Technical Summary

Benefits of technology

[0012]Ink database searching using handwriting feature synthesis allows a digital ink database to be searched using a text-based query. Using a writer-specific handwriting model derived from a handwriting recognition system or suitable training procedure, a text query is converted into feature vectors that are similar to the feature vectors that would have been extracted had the author of the digital ink database written the text query by hand. The feature vectors are then used to search the database, for example by using traditional techniques. This allows the searching of a digital ink database when the only input mechanism available is text entry, and can allow a person other than the author of the digital ink database to search the digital ink database.

Problems solved by technology

However, searching handwritten text is more difficult than traditional text (e.g. ASCII text) searching due to inconsistencies in the production of handwriting and the stylistic variations between writers.

However, handwriting recognition accuracy remains low, and the number of errors introduced by handwriting recognition (both for the database entries and for the handwritten query) means that this technique does not work well.

The process of converting handwritten information into text results in the loss of a significant amount of information regarding the general shape and dynamic properties of the handwriting.

Additionally, in many handwriting styles (particularly cursive writing), the identification of individual characters is highly ambiguous.

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