# US 2010/0166318 A1 · Adaptive partial character recognition

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- Number: US 2010/0166318 A1
- Document ID: US20100166318A1
- Status: Published application
- Kind: A1
- Application number: 12/346,221
- Filed: 2008-12-30
- Published: 2010-07-01
- Earliest claimed: 2008-12-30
- Assignees: International Business Machines Corporation
- Inventors: Ami Ben-Horesh; Amir Geva; Eugeniusz Walach
- Web page: https://patentgrep.com/patent/US20100166318A1

## CPC

- G06V 30/155: {Removing patterns interfering with the pattern to be recognised, such as ruled lines or underlines}
- G06V 30/1902: {Shifting or otherwise transforming the patterns to accommodate for positional errors}
- 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)}
- G06V 30/10: Character recognition
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## 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

### Claim 1

A system for recognizing a character with a noise or an obstruction in an image, the image including a plurality of characters, comprising:
means for detecting the noise or the obstruction in the character;
means for providing one or more templates of the plurality of characters;
means for updating the one or more templates of the plurality of characters based on the detected noise or the detected obstruction; and
means for classifying the character in one of the updated templates of the plurality of characters, the character being recognized as a letter, number or symbol that the template, which the character is classified in, represents.

### Claim 2 (depends on 1)

The system according to claim 1, further comprising:
means for calculating a stroke feature of unobstructed areas of the character, the unobstructed areas of the character not having any effect from a noise or an obstruction; and
means for calculating statistics of adjacent characters of the character, the adjacent characters not having any effect from a noise or an obstruction.

### Claim 3 (depends on 2)

The system according to claim 2, wherein the means for detecting performs finding at least one area of the character in which a stroke feature does not conform to the calculated stroke feature of the unobstructed areas or in which statistics does not conform to the calculated statistics of the adjacent characters.

### Claim 4 (depends on 3)

The system according to claim 3, wherein the means for updating comprises:
means for removing the at least one area in the one or more templates; and
means for removing the at least one area in the character.

### Claim 5 (depends on 3)

The system according to claim 3, wherein the means for classifying comprises:
means for counting the number of matching first-type pixels in a template and the character;
means for counting the number of matching second-type pixels in the template and the character;
means for setting pixels in the at least one area in the character as don't care pixels;
means for counting the number of don't care pixels in the character;
means for calculating a value of (the number of matching first-type pixels+the number of matching second-type pixels−the number of don't care pixels) for each pair of each template and the character;
means for selecting a pair of a template and the character that has a highest calculated value among the calculated values of pairs of each template and the character; and
means for identifying the character as the letter, the number or the symbol that the template in the selected pair represents.

### Claim 6 (depends on 2)

The system according to claim 2, wherein the stroke feature is 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.

### Claim 7 (depends on 2)

The system according to claim 2, wherein the statistics is one or more of
a font height, an ascent value and a descent value.

### Claim 8 (depends on 2)

The system according to claim 2, further comprising:
means for dividing the character into parts; and
means for analyzing each part to determine whether the part is affected by the noise or the obstruction.

### Claim 9 (depends on 8)

The system according to claim 8, further comprising:
means for further dividing a part into subparts, if the part is affected by the noise or the obstruction;
means for finding at least one area in a subpart, the at least one area does not conform to the calculated stroke feature.

### Claim 10 (depends on 1)

The system according to claim 1, wherein the means for classifying performs a comparison between the updated templates and the character to recognize the character.

### Claim 11 (depends on 10)

The system according to claim 10, wherein the means for classifying performs the comparison based on one or more of: correlating each of the updated templates with the character, finding difference between each of the updated templates and the character, and utilizing a trained neural network to identify the character.

### Claim 12

A method for recognizing a character with a noise or an obstruction in an image, the image including a plurality of characters, comprising:
detecting the noise or the obstruction in the character;
providing one or more templates of the plurality of characters;
updating the one or more templates of the plurality of characters based on the detected noise or the detected obstruction; and
classifying the character in one of the updated templates of the plurality of characters, the character being recognized as a letter, number or symbol that the template, which the character is classified in, represents.

### Claim 13 (depends on 12)

The method according to claim 12, further comprising:
calculating a stroke feature of unobstructed areas of the character, the unobstructed areas of the character not having any effect from a noise or an obstruction; and
calculating statistics of adjacent characters of the character, the adjacent characters not having any effect from a noise or an obstruction.

### Claim 14 (depends on 13)

The method according to claim 13, wherein the detecting performs finding at least one area of the character in which a stroke feature does not conform to the calculated stroke feature of the unobstructed areas or in which statistics does not conform to the calculated statistics of the adjacent characters.

### Claim 15 (depends on 14)

The method according to claim 14, wherein the updating comprises:
removing the at least one area in the one or more templates; and
removing the at least one area in the character.

### Claim 16 (depends on 14)

The method according to claim 14, wherein the classifying comprises:
counting the number of matching first-type pixels in a template and the character;
counting the number of matching second-type pixels in the template and the character;
setting pixels in the at least one area in the character as don't care pixels;
counting the number of don't care pixels in the character;
calculating a value of (the number of matching first-type pixels+the number of matching second-type pixels−the number of don't care pixels) for each pair of each template and the character;
selecting a pair of a template and the character that has a highest calculated value among the calculated values of pairs of each template and the character; and
identifying the character as the letter, the number or the symbol that the template in the selected pair represents.

### Claim 17 (depends on 13)

The method according to claim 13, wherein the stroke feature is 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.

### Claim 18 (depends on 13)

The system according to claim 13, wherein the statistics is one or more of:
a font height, an ascent value and a descent value.

### Claim 19 (depends on 13)

The method according to claim 13, further comprising:
dividing the character into parts; and
analyzing each part to determine whether the part is affected by the noise or the obstruction.

### Claim 20 (depends on 19)

The method according to claim 19, further comprising:
further dividing a part into subparts, if the part is affected by the noise or the obstruction;
finding at least one area in a subpart, the at least one area does not conform to the calculated stroke feature.

### Claim 21 (depends on 12)

The method according to claim 12, wherein the classifying performs a comparison between the updated templates and the character to recognize the character.

### Claim 22 (depends on 21)

The method according to claim 21, wherein the classifying performs the comparison based on one or more of: correlating each of the updated templates with the character, finding difference between each of the updated templates and the character, and utilizing a trained neural network to identify the character.

### Claim 23 (depends on 12)

A computer readable medium embodying computer program instructions being executed by a processor for causing a computer to perform method steps for recognizing a character with a noise or an obstruction in an image, the image including a plurality of characters, said method steps comprising the steps of claim 12.

### Claim 24 (depends on 12)

A method of deploying a computer program product including a program of instructions in a computer readable medium for recognizing a character with a noise or an obstruction in an image, the image including a plurality of characters, wherein, when the program of instructions is executed by a processor, the computer program product performs the steps of claim 12.