Natural Language Recognition Using Context Information

• US 20100125451A1
• Filed: 01/21/2010
• Published: 05/20/2010
• Est. Priority Date: 08/15/2003
• Status: Abandoned Application

• Current Assignee: Silverbrook Research Pty Limited
• Original Assignee: Silverbrook Research Pty Limited
• Inventors: Silverbrook, Kia, Napper, Jonathon Leigh, Lapstun, Paul
• Application Number:
  US12/691,725
• Publication Number:
  US 20100125451A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/171 by use of digital ink
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06V 30/226 of cursive writing
• CPC Class Codes: G06V 30/242 Division of the character sequences into groups prior to recognition; Selection of dictionaries
• CPC Class Codes: G10L 15/30 Distributed recognition, e.g. in client-server systems, for mobile phones or network applications

Abstract

A method of recognising digital ink input by a user into a computer-based digital ink recognition system is disclosed. The user interacts with a paper-based document. The paper-based document has disposed thereon coded data indicative of a particular field of the paper-based document and of at least one reference point of the paper-based document. An image sensor in a sensing device captures images of at least some of the coded data when the sensing device is placed in an operative position relative to the paper-based document. The sensing device then decodes at least some of the coded data to form indicating data indicative of the identity of the field of the paper-based document containing the coded data and at least one of a position and a movement of the sensing device relative to the paper-based document. A server receives the indicating data from the sensing device, and processes the indicating data using a recognizer residing on the server to produce intermediate format data. The intermediate format data is then transmitted to an application which decodes the intermediate format data into computer-readable format data using context information associated with the paper-based document.

Claims

1. A method of recognising digital ink input by a user into a computer-based digital ink recognition system, the user interacting with a paper-based document, the paper-based document having disposed thereon coded data indicative of a particular field of the paper-based document and of at least one reference point of the paper-based document, the method including the steps of:

capturing by an image sensor in a sensing device images of at least some of the coded data when the sensing device is placed in an operative position relative to the paper-based document;

decoding by the sensing device at least some of the coded data to form indicating data indicative of the identity of the field of the paper-based document containing the coded data and at least one of a position and a movement of the sensing device relative to the paper-based document;

receiving in a server the indicating data from the sensing device;

processing the indicating data using a recognizer residing on the server to produce intermediate format data;

receiving the intermediate format data in an application;

decoding the intermediate format data by the application into computer-readable format data using context information associated with the paper-based document.

2. The method as claimed in claim 1, wherein the particular field of the paper-based document is associated with at least one zone of the paper-based document, and the method includes identifying the context information from the at least one zone.

3. The method as claimed in claim 1, wherein the intermediate format data is a Directed Acyclic Graph (DAG) data structure.

4. The method as claimed in claim 3, wherein the DAG data structure is a matrix containing the processing results of segments of the indicating data.

5. The method as claimed in claim 1, wherein the intermediate format data includes segmented time-series classifier data.

6. The method as claimed in claim 1, wherein the context information is a user dictionary.

7. The method as claimed in claim 1, wherein the recognizer is trained for a specific user.