# US 9,519,838 B2 · Character recognition method

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- Number: US 9,519,838 B2
- Document ID: US9519838B2
- Status: Granted
- Kind: B2
- Application number: 14/759,256
- Filed: 2013-06-26
- Published: 2015-12-10
- Granted: 2016-12-13
- Earliest claimed: 2013-01-11
- Assignees: GRG Banking Equipment Co., Ltd.
- Applicants: GRG Banking Equipment Co., Ltd.
- Inventors: Tiancai Liang; Kun Wang; Weifeng Wang; Siwei Liu
- Web page: https://patentgrep.com/patent/US9519838B2

## CPC

- G06V 30/182: by coding the contour of the pattern
- G06T 5/70: Denoising; Smoothing
- G06T 7/12: Edge-based segmentation
- G06T 7/13: Edge detection
- G06V 30/10: Character recognition
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

The present invention relates to a character recognition method. Same comprises: step 1: reading in a character image; step 2: image preprocessing; step 3: character splitting; step 4: edge extraction; utilizing an edge detection operator to detect edge points of characters; step 5: feature extraction, where the features of each edge point of each character are all expressed with distances running from other edge points of the character to the edge point and are represented by eigenvectors (Pk, Pk, . . . P); step 6: feature processing, mapping the eigenvectors (Pk, Pk, . . . P) into a matrix, T, thus allowing all characters to be identical in eigenvector dimension; and, step 7: template matching recognition. Because the features of each edge point of the characters are all expressed with the distances running from the other edge points to the edge point, the entirety of the features are ensured for the characters, and the difference in features are expanded for different characters, thus increasing character recognition accuracy.

## Claims

### Claim 1

A character recognition method, comprising the steps of:
1: reading, by a processor, a character image;
2: performing image preprocessing on the read character image;
3: performing character segmentation on the preprocessed character image;
4: edge extraction, comprising detecting edge points of the character by using an edge detecting operator;
5: feature extraction, performing expressing features of each edge point of each character by distances from edge points of the character other than the edge point to the edge point, wherein the features of the edge point of the character are represented by an eigenvector (P1k, P2k . . . PMk), thus eigenvectors of features of the character are represented by a matrix
(
P
21
P
31
…
P
M
⁢
⁢
1
P
12
P
32
…
P
M
⁢
⁢
2
⋮
⋮
⋱
⋮
P
1
⁢
⁢
M
P
2
⁢
⁢
M
…
P
(
M
-
1
)
⁢
M
)
;
6: feature processing, performing mapping the eigenvector (P1k, P2k . . . PMk) to a matrix T, to obtain a same dimension for eigenvectors of all characters, wherein
T
=
(
t
11
t
12
…
t
1
⁢
⁢
v
t
21
t
22
…
t
2
⁢
⁢
v
⋮
⋮
⋱
⋮
t
u
⁢
⁢
1
t
u
⁢
⁢
2
…
t
uv
)
;
and
7: template matching comprising assuming that an edge of a character Θ to be recognized has M points X1, X2 . . . XM and a standard character template Δ has N points Y1, Y2 . . . YN calculating a distance between the point Xi and the point Yj in the standard character template Δ by an equation as:
D
ij
≡
D
⁡
(
X
i
,
Y
j
)
=
∑
m
=
1
u
⁢
∑
n
=
1
v
⁢
(
T
i
⁡
(
m
,
n
)
-
T
j
⁡
(
m
,
n
)
)
2
;
denoting Diπ(i)=min Dij, which indicates that a point Yπ(i) in the standard character template Δ is a best matching point for the point Xi,
a total distance from the character Θ to be recognized to the standard character template Δ being
D
Θ
⁢
⁢
Δ
=
∑
i
=
0
M
⁢
D
i
⁢
⁢
π
⁡
(
i
)
;
and
in a case that there are total Γ standard character templates, calculating total distances from the character Θ to be recognized to each standard character template respectively, and a template with a minimal distance to the character is a recognition result for the character.

### Claim 2 (depends on 1)

The character recognition method according to claim 1, wherein the mapping the eigenvector (P1k, P2k. . . PMk) to a matrix T in step 6 comprises:
step 61 comprising: assuming one component Pij of an eigenvector of any point of edge of a character as Pij=(lij, θij), wherein lij∈[0, L] and θij∈[0,2π], forming a block matrix by dividing the interval [0, L] into u equal parts and dividing the interval [0,2π] into v equal parts, and mapping each component of the eigenvector of any point of the edge of the character to the block matrix according to values of lij and θij; and
step 62 comprises: setting that a component tij of T is the number of eigen components Pijmapped to a jth block in an ith row, then the eigenvector (P1k, P2k. . . PMk) of any point is converted into a u×v matrix T; and
step 63 comprising: representing all features of each character as
(
T
1
T
2
⋮
T
M
)
,
wherein each component Ti is a u×v matrix.

### Claim 3 (depends on 1)

The character recognition method according to claim 1, wherein the image preprocessing in step 2 comprises image denoising, image rotation correction and image binarization.

### Claim 4 (depends on 3)

The character recognition method according to claim 3, wherein the character segmentation in step 3 comprises: performing horizontal projection and vertical projection on the binarized character image to segment the binarized character image into characters.