SYSTEM AND METHOD FOR INPUT RECOGNITION LINGUISTIC RESOURCE MANAGEMENT

• US 20170351420A1
• Filed: 07/21/2016
• Published: 12/07/2017
• Est. Priority Date: 06/02/2016
• Status: Active Grant

• Current Assignee: Myscript
• Original Assignee: Myscript
• Inventors: Rigouste, Lois
• Granted Patent: US 11,262,909 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 2203/0381 Multimodal input, i.e. interface arrangements enabling the user to issue commands by simultaneous use of input devices of different nature, e.g. voice plus gesture on digitizer
• CPC Class Codes: G06F 3/0233 Character input methods
• CPC Class Codes: G06F 3/0237 using prediction or retrieval techniques
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06F 3/04886 by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs

Abstract

A system, method and computer program product for use in providing a linguistic resource for input recognition of multiple input types on a computing device are provided. The computing device is connected to an input interface. A user is able to provide input by applying pressure to or gesturing above the input interface using a finger or an instrument such as a stylus or pen. The computing device has an input management system for recognizing the input. The input management system is configured to allow setting, in the computing device memory, parameters of a linguistic resource for a language model of one or more languages, and cause recognition of input to the input interface of the different input types using the linguistic resource. The resource parameters are set to optimize recognition performance characteristics of each input type while providing the linguistic resource with the pre-determined size.

Claims

1. A system for providing a linguistic resource for input recognition of multiple input types to computing devices, each computing device comprising a processor, a memory and at least one non-transitory computer readable medium for recognizing input under control of the processor, the at least one non-transitory computer readable medium configured to:

allow setting, in a memory of a computing device, of a plurality of parameters of a linguistic resource which provides a language model of one or more languages; and

cause recognition of input, to an input interface of the computing device, of one or more types of a plurality of different types of input in said one or more languages using said linguistic resource,

wherein the plurality of parameters are set in order to optimize performance characteristics of said recognition of each of the one or more types of recognized input while providing the linguistic resource with a pre-determined size.

2. A system according to claim 1, wherein the different types of input include handwriting input and typing input.

3. A system according to claim 2, wherein the performance characteristics include the word recognition rate of the handwriting input, the word prediction rate of the typing input and the keystroke saving rate of the typing input.

4. A system according to claim 3, wherein:

the language model is a class-based n-gram language model utilizing a lexicon built using one or more corpora related to said one or more languages; and

the plurality of parameters include one or more of a first parameter related to the size of the lexicon, a second parameter related to the corpora content used to build the lexicon and the n-gram model, a third parameter related to the number of n-gram sequences, a fourth parameter related to the order of the n-gram model, and a fifth parameter related to the number of classes in the class-based model.

5. A system according to claim 4, wherein the fourth and fifth parameters are set to optimize the word prediction rate of the typing input.

6. A system according to claim 5, wherein the first parameter is set to optimize the word recognition rate, the second parameter is set to optimize the word prediction and keystroke saving rates of the typing input, and the third parameter is set to provide the linguistic resource with said pre-determined size once the performance characteristics have been optimized.

7. A method for providing a linguistic resource for input recognition of multiple input types to computing devices, each computing device comprising a processor and at least one non-transitory computer readable medium for recognizing input under control of the processor, the method comprising:

allowing setting, in a memory of a computing device, of a plurality of parameters of a linguistic resource which provides a language model of one or more languages; and

causing recognition of input, to an input interface of the computing device, of one or more types of a plurality of different types of input in said one or more languages using said linguistic resource,

wherein the plurality of parameters are set in order to optimize performance characteristics of said recognition of each of the one or more types of recognized input while providing the linguistic resource with a pre-determined size.

8. A method according to claim 7, wherein the different types of input include handwriting input and typing input.

9. A method according to claim 8, wherein the performance characteristics include the word recognition rate of the handwriting input, the word prediction rate of the typing input and the keystroke saving rate of the typing input.

10. A method according to claim 9, wherein:

the language model is a class-based n-gram language model utilizing a lexicon built using one or more corpora related to said one or more languages; and

the plurality of parameters include one or more of a first parameter related to the size of the lexicon, a second parameter related to the corpora content used to build the lexicon and the n-gram model, a third parameter related to the number of n-gram sequences, a fourth parameter related to the order of the n-gram model, and a fifth parameter related to the number of classes in the class-based model.

11. A method according to claim 10, wherein the fourth and fifth parameters are set to optimize the word prediction rate of the typing input.

12. A method according to claim 11, wherein the first parameter is set to optimize the word recognition rate, the second parameter is set to optimize the word prediction and keystroke saving rates of the typing input, and the third parameter is set to provide the linguistic resource with said pre-determined size once the performance characteristics have been optimized.

13. A non-transitory computer readable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for providing a linguistic resource for input recognition of multiple input types to a computing device, the computing device comprising a processor and at least one system non-transitory computer readable medium for recognizing input under control of the processor, the method comprising:

allowing setting, in a memory of a computing device, of a plurality of parameters of a linguistic resource which provides a language model of one or more languages; and

causing recognition of input, to an input interface of the computing device, of one or more types of a plurality of different types of input in said one or more languages using said linguistic resource,

wherein the plurality of parameters are set in order to optimize performance characteristics of said recognition of each of the one or more types of recognized input while providing the linguistic resource with a pre-determined size.

14. A method according to claim 13, wherein the different types of input include handwriting input and typing input.

15. A method according to claim 14, wherein the performance characteristics include the word recognition rate of the handwriting input, the word prediction rate of the typing input and the keystroke saving rate of the typing input.

16. A method according to claim 15, wherein:

the language model is a class-based n-gram language model utilizing a lexicon built using one or more corpora related to said one or more languages; and

the plurality of parameters include one or more of a first parameter related to the size of the lexicon, a second parameter related to the corpora content used to build the lexicon and the n-gram model, a third parameter related to the number of n-gram sequences, a fourth parameter related to the order of the n-gram model, and a fifth parameter related to the number of classes in the class-based model.

17. A method according to claim 16, wherein the fourth and fifth parameters are set to optimize the word prediction rate of the typing input.

18. A method according to claim 17, wherein the first parameter is set to optimize the word recognition rate, the second parameter is set to optimize the word prediction and keystroke saving rates of the typing input, and the third parameter is set to provide the linguistic resource with said pre-determined size once the performance characteristics have been optimized.