ADAPTIVE PARTIAL CHARACTER RECOGNITION

• US 20130011067A1
• Filed: 09/14/2012
• Published: 01/10/2013
• Est. Priority Date: 12/30/2008
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ben-Horesh, Ami, Geva, Amir, Walach, Eugeniusz
• Granted Patent: US 8,594,431 B2
• Time in Patent Office: Days
• US Class Current: 382/189
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/155 Removing patterns interfering with the pattern to be recognised, such as ruled lines or underlines
• CPC Class Codes: G06V 30/1902 Shifting or otherwise transforming the patterns to accommodate for positional errors
• CPC Class Codes: G06V 30/248 involving plural approaches, e.g. verification by template match; Resolving confusion among similar patterns, e.g. "O" versus "Q" G06V30/242 takes precedence

Abstract

A method and system for recognizing a character affected by a noise or an obstruction is disclosed. After receiving an image with characters, a character being affected by a noise or an obstruction is determined. Then, areas in the character where the noise or obstruction affected are precisely located. Templates representing every possible character in the image are updated by removing equivalent areas to the areas in the character being affected by the noise or obstruction. Then, the character is classified in a template among the updated templates by finding the template having the highest number of matching pixels with the character.

Claims

1. A system for identifying a character with a noise or an obstruction, said system comprising:

a memory device;

a processor coupled to said memory device,

wherein said processor is configured to perform steps of;

measuring a stroke feature of said character;

measuring a stroke feature of adjacent characters of said character;

detecting, based on said measured stroke feature of said character and based on said measured stroke feature of said adjacent characters, said noise or said obstruction in said character;

receiving one or more templates of a plurality of characters;

updating said one or more received templates based on said detected noise or said detected obstruction; and

classifying said character in one of said one or more updated templates.

2. The system according to claim 1, wherein said stroke feature represents one or more of:

a movement of a pen, a movement of pencil, a movement of a stylus, a movement of a digital writing tablet, a movement of a digital touch screen interface.

3. The system according to claim 1, wherein said stroke feature includes one or more of:

a stroke width, a stroke length, a stroke size, a stroke duration, a stroke orientation, a stroke shape and a stroke pattern.

4. The system according to claim 1, wherein said processor is further configured to perform a step of:

measuring statistics of said adjacent characters.

5. The system according to claim 4, wherein said statistics includes one or more of:

a font height, an ascent value, and a descent value.

6. The system according to claim 1, wherein in order to detect said noise or said obstruction, said processor is further configured to perform steps of:

finding an area of said character in which said measured stroke feature of said character does not conform to said measured stroke feature of said adjacent characters.

7. The system according to claim 6, wherein said processor is further configured to perform steps of:

dividing said character into parts;

analyzing each part to determine whether said each part is affected by said noise or said obstruction by comparing a stroke feature of said each part to said measured stroke feature of said adjacent characters;

upon determining a particular part is affected by said noise or said obstruction, dividing said particular part into subparts; and

analyzing each subpart to determine whether said each subpart is affected by said noise or said obstruction by comparing a stroke feature of said each subpart to said measured stroke feature of said adjacent characters.

8. The system according to claim 7, in order to update said one or more templates, wherein the processor is further configured to perform steps of:

removing areas in said one or more templates, said removed area being equivalent to areas, in said character, which are affected by said detected noise or said obstruction.

9. The system according to claim 8, wherein said processor is further configured to perform steps of:

removing said affected areas in said character.

10. The system according to claim 1, wherein in order to classify said character, said processor is further configured to perform steps of:

counting a number of matching first-type pixels in a template and said character;

counting a number of matching second-type pixels in said template and said character;

setting pixels in at least one area in said character as don'"'"'t care pixels, said at least one area being affected by said noise or said obstruction;

counting a number of said don'"'"'t care pixels in said character;

calculating a value of (said number of matching first-type pixels+said number of matching second-type pixels−

said number of don'"'"'t care pixels) for each pair of each template and said character;

selecting a pair of a template and said character that has a highest calculated value among the calculated values of pairs of each template and said character; and

identifying said character as a letter, a number or a symbol that said template in said selected pair represents.

11. A method for identifying a character with a noise or an obstruction, said method comprising:

measuring a stroke feature of said character;

measuring a stroke feature of adjacent characters of said character;

detecting, based on said measured stroke feature of said character and based on said measured stroke feature of said adjacent characters, said noise or said obstruction in said character;

receiving one or more templates of a plurality of characters;

updating said one or more received templates based on said detected noise or said detected obstruction; and

classifying said character in one of said one or more updated templates.

12. The method according to claim 11, wherein said stroke feature represents one or more of:

a movement of a pen, a movement of pencil, a movement of a stylus, a movement of a digital writing tablet, a movement of a digital touch screen interface.

13. The method according to claim 11, wherein said stroke feature includes one or more of:

a stroke width, a stroke length, a stroke size, a stroke duration, a stroke orientation, a stroke shape and a stroke pattern.

14. The method according to claim 11, further comprising:

measuring statistics of said adjacent characters.

15. The method according to claim 14, wherein said statistics includes one or more of:

a font height, an ascent value, and a descent value.

16. The method according to claim 11, wherein said detecting includes a step of:

finding an area of said character in which said measured stroke feature of said character does not conform to said measured stroke feature of said adjacent characters.

17. The method according to claim 16, further comprising:

dividing said character into parts;

analyzing each part to determine whether said each part is affected by said noise or said obstruction by comparing a stroke feature of said each part to said measured stroke feature of said adjacent characters;

upon determining a particular part is affected by said noise or said obstruction, dividing said particular part into subparts; and

analyzing each subpart to determine whether said each subpart is affected by said noise or said obstruction by comparing a stroke feature of said each subpart to said measured stroke feature of said adjacent characters.

18. The method according to claim 17, wherein said updating includes a step of:

removing areas in said one or more templates, said removed area being equivalent to areas, in said character, which are affected by said detected noise or said obstruction.

19. The method according to claim 18, further comprising:

removing said affected areas in said character.

20. The system according to claim 11, wherein said classifying includes steps of:

counting a number of matching first-type pixels in a template and said character;

counting a number of matching second-type pixels in said template and said character;

setting pixels in at least one area in said character as don'"'"'t care pixels, said at least one area being affected by said noise or said obstruction;

counting a number of said don'"'"'t care pixels in said character;

calculating a value of (said number of matching first-type pixels+said number of matching second-type pixels−

said number of don'"'"'t care pixels) for each pair of each template and said character;

selecting a pair of a template and said character that has a highest calculated value among the calculated values of pairs of each template and said character; and

identifying said character as a letter, a number or a symbol that said template in said selected pair represents.

21. A computer program product for identifying a character with a noise or an obstruction, the computer program product comprising a non-transitory storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, said method steps comprising:

measuring a stroke feature of said character;

measuring a stroke feature of adjacent characters of said character;

detecting, based on said measured stroke feature of said character and based on said measured stroke feature of said adjacent characters, said noise or said obstruction in said character;

receiving one or more templates of a plurality of characters;

updating said one or more received templates based on said detected noise or said detected obstruction; and

classifying said character in one of said one or more updated templates.

22. The computer program product according to claim 21, wherein said classifying includes steps of:

counting a number of matching first-type pixels in a template and said character;

counting a number of matching second-type pixels in said template and said character;

setting pixels in at least one area in said character as don'"'"'t care pixels, said at least one area being affected by said noise or said obstruction;

counting a number of said don'"'"'t care pixels in said character;

calculating a value of (said number of matching first-type pixels+said number of matching second-type pixels−

said number of don'"'"'t care pixels) for each pair of each template and said character;

selecting a pair of a template and said character that has a highest calculated value among the calculated values of pairs of each template and said character; and

identifying said character as a letter, a number or a symbol that said template in said selected pair represents.