Granted Patent B2

US 8,180,160 · App. 11/947,197 · Granted May 15, 2012

Abstract

Claims (70)

1. A method for classifying a line segment of a handwritten line into a reference feature set, wherein said handwritten line comprises one or several curves representing a plurality of symbols, said method comprising:

receiving sample data representing said handwritten line;

identifying a sample line segment in said received sample data by detecting a sample line segment start point (SLSSP) and a sample line segment end point (SLSEP);

determining a sample feature set of said identified sample line segment, wherein said determination of said sample feature set further comprises:

determining a first angle between a gradient of said line segment in said SLSSP and a segment specific reference line intersecting said SLSSP and said SLSEP;

associating said first angle to said sample feature set;

determining a second angle between a gradient of said line segment in said SLSEP and a segment specific reference line intersecting said SLSSP and said SLSEP;

associating said second angle to said sample feature set;

determining a general angle between the segment specific reference line intersecting said SLSSP and said SLSEP and a reference line corresponding to a writing direction; and

associating said general angle to said sample feature set; and

matching said determined sample feature set to a reference feature set among a plurality of reference feature sets,

wherein the method is performed using a computer or processor.

2. The method according to claim 1 , wherein said steps of receiving, identifying, determining, and matching are performed successively.

3. The method according to claim 1 , wherein said plurality of reference feature sets are predetermined.

4. The method according to claim 1 , wherein said determination of said sample feature set further comprises: determining a length between said SLSSP and said SLSEP; and associating said length to said sample feature set.

5. The method according to claim 4 , wherein said determination of said length further comprises: generating a normalized length of said length by taking prior determined lengths into account.

6. The method according to claim 1 , wherein said determination of said sample feature set further comprises: determining a type curve matching said line segment; and associating said type curve to said sample feature set.

7. The method according to claim 1 , wherein said reference feature set comprises a minimum value and a maximum value for each feature.

8. The method according to claim 1 , wherein said sample data comprises coordinate data.

9. A hardware-implemented module for classifying a line segment of a handwritten line into one reference line segment of a reference line segment set, wherein said handwritten line comprises one or several curves representing a plurality of symbols, said module comprising:

a receiver configured to receive data representing said handwritten line;

an identifier configured to identify a sample line segment in said received data by detecting a sample line segment start point (SLSSP) and a sample line segment end point (SLSEP);

a determinator configured to determine a sample feature set for said identified sample line segment, wherein said determinator further comprises:

an angle determinator configured to determine an angle between said line segment and a reference line intersecting said SLSSP and said SLSEP;

an associater configured to associate said determined angle to said sample feature set;

a general angle determinator configured to determine a general angle between a segment specific reference line intersecting said SLSSP and said SLSEP and a reference line corresponding to a writing direction;

an associater configured to associate said determined general angle to said sample feature set; and

a matcher configured to match said determined sample feature set to a reference feature set among a plurality of reference feature sets; and

a transmitter configured to output said matching reference feature set.

10. The hardware-implemented module according to claim 9 , wherein said determinator further comprises: a length determinator configured to determine a length between said SLSSP and said SLSEP; and an associater configured to associate said determined length to said sample feature set.

11. The hardware-implemented module according to claim 10 , wherein said determinator further comprises: a storage medium comprising a number of prior determined lengths; a length transformer configured to normalize said length to a normalized length by taking said prior determined lengths comprised within said storage medium into account; and an associater configured to associate said determined normalized length to said sample feature set.

12. The hardware-implemented module according to claim 9 , wherein said determinator further comprises: a type curve determinator configured to determine a type curve matching said line segment; and an associater configured to associate said determined type curve to said sample feature set.

13. An apparatus comprising:

a pen movement capturing device configured to receive data representing a handwritten line;

a module according to claim 9 configured to receive said data from said touch sensitive area and to output reference feature sets;

a symbol matcher configured to match said reference feature sets into a symbol set corresponding to a symbol; and

a display configured to present said symbol.

14. The apparatus according to claim 13 , wherein said pen movement capturing device is a touch sensitive area.

15. The apparatus according to claim 13 , further comprising a symbol set database comprising a number of reference line segment combinations and their associated symbol sets.

16. A computer readable memory device having stored thereon instructions that are executable by a computer to classify a line segment of a handwritten line into a reference feature set, wherein the handwritten line comprises one or several curves representing a plurality of symbols, the instructions causing the computer to (a) receive sample data representing the handwritten line, (b) identify a sample line segment in the received sample data by detecting a sample line segment start point (SLSSP) and a sample line segment end point (SLSEP), (c) determine a sample feature set of the identified sample line segment, determine a first angle between a gradient of said line segment in said SLSSP and a segment specific reference line intersecting said SLSSP and said SLSEP, associate said first angle to said sample feature set, determine a second angle between a gradient of said line segment in said SLSEP and a segment specific reference line intersecting said SLSSP and said SLSEP, associate said second angle to said sample feature set, determine a general angle between the segment specific reference line intersecting said SLSSP and said SLSEP and a reference line corresponding to a writing direction, and associate said general angle to said sample feature set, and (d) match the determined sample feature set to a reference feature set among a plurality of reference feature sets.

17. A method for classifying a line segment of a handwritten line into a reference feature set, wherein said handwritten line comprises one or several curves representing a plurality of symbols, said method comprising:

receiving sample data representing said handwritten line;

identifying a sample line segment in said received sample data by detecting a sample line segment start point (SLSSP) and a sample line segment end point (SLSEP);

determining a sample feature set of said identified sample line segment, wherein said determination of said sample feature set further comprises:

determining a first angle between a gradient of said line segment in said SLSSP and a segment specific reference line intersecting said SLSSP and said SLSEP;

associating said first angle to said sample feature set;

determining a second angle between a gradient of said line segment in said SLSEP and a segment specific reference line intersecting said SLSSP and said SLSEP;

associating said second angle to said sample feature set;

determining a general angle between the segment specific reference line intersecting said SLSSP and said SLSEP and a reference line corresponding to a writing direction;

associating said general angle to said sample feature set;

determining a type curve matching said line segment; and

associating said type curve to said sample feature set; and

matching said determined sample feature set to a reference feature set among a plurality of reference feature sets,

wherein the method is performed using a computer or processor.

18. The method according to claim 17 , wherein said steps of receiving, identifying, determining, and matching are performed successively, wherein said plurality of reference feature sets are predetermined.

19. The method according to claim 17 , wherein said determination of said sample feature set further comprises: determining a length between said SLSSP and said SLSEP; and associating said length to said sample feature set.

20. The method according to claim 19 , wherein said determination of said length further comprises: generating a normalized length of said length by taking prior determined lengths into account.

21. A hardware-implemented module for classifying a line segment of a handwritten line into one reference line segment of a reference line segment set, wherein said handwritten line comprises one or several curves representing a plurality of symbols, said module comprising:

a receiver configured to receive data representing said handwritten line;

an identifier configured to identify a sample line segment in said received data by detecting a sample line segment start point (SLSSP) and a sample line segment end point (SLSEP);

a determinator configured to determine a sample feature set for said identified sample line segment, wherein said determinator further comprises:

an angle determinator configured to determine an angle between said line segment and a reference line intersecting said SLSSP and said SLSEP;

an associater configured to associate said determined angle to said sample feature set;

a general angle determinator configured to determine a general angle between a segment specific reference line intersecting said SLSSP and said SLSEP and a reference line corresponding to a writing direction;

an associater configured to associate said determined general angle to said sample feature set; a type curve determinator configured to determine a type curve matching said line segment; and

an associater configured to associate said determined type curve to said sample feature set;

a matcher configured to match said determined sample feature set to a reference feature set among a plurality of reference feature sets; and

a transmitter configured to output said matching reference feature set.

22. The hardware-implemented module according to claim 21 , wherein said determinator further comprises: a length determinator configured to determine a length between said SLSSP and said SLSEP; and an associater configured to associate said determined length to said sample feature set.

23. The hardware-implemented module according to claim 22 , wherein said determinator further comprises: a storage medium comprising a number of prior determined lengths; a length transformer configured to normalize said length to a normalized length by taking said prior determined lengths comprised within said storage medium into account; and an associater configured to associate said determined normalized length to said sample feature set.

Assignments (9)