CN115331231A - Method for recognizing target text based on text, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to a method for recognizing a target text based on a text, which comprises the following steps: processing the target image to acquire polygonal labeling information corresponding to the text recognition area; based on the labeling information of the polygon, reducing the inward length to L to obtain a first label; based on the labeling information of the polygon, expanding the outward length of L to obtain a second label; inputting an image processing model based on the first label, the second label and the target image, and acquiring a final text recognition area; acquiring a target character string based on the final text recognition area; and introducing preset reduction parameters to adaptively reduce the length and the width of the rectangle, and finally obtaining the target characters.
Description
Technical Field
The present invention relates to the field of semantic analysis, and in particular, to a method, an electronic device, and a storage medium for recognizing a target text based on a text.
Background
Currently, semantic segmentation is one of the key problems in the current computer vision field, and from a macroscopic point of view, semantic segmentation is a high-level task, a road is paved for realizing complete understanding of a scene, and the understanding of the scene is taken as a core computer vision problem, and the importance of the computer vision problem is that more and more application programs infer knowledge from an image, wherein the knowledge includes some automatic driving, heat engine interaction and the like, and character detection based on semantic segmentation needs to be reduced in a recognition process, but in a reduction process, as the difference of the aspect ratio of a text recognition area increases, the reduction part also increases, so that the text recognition area cannot completely cover the upper and lower boundaries of characters.
Disclosure of Invention
Aiming at the technical problems, the technical scheme adopted by the invention is as follows: a method of recognizing a target text based on a text, the method comprising the steps of:
s301, processing the target image to acquire polygon labeling information corresponding to the text recognition area;
s302, based on the labeling information of the polygon, the inward height and length of the polygon are reduced to obtain a first label, wherein,
L＝L 1 -[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H＝H 1 -[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 ) Wherein H is 1 Height, L, of the rectangle to which the polygon is labeled 1 The length of the rectangle to be marked for the polygon, r is an empirical factor, H is the height of the rectangle to be marked for the polygon after reduction, L is the height of the rectangle to be marked for the polygonThe length of the rectangle marked by the reduced polygon is k, and k is a preset reduction parameter;
s303, expanding the outward height and length of the polygon based on the labeling information of the polygon to obtain a second label,
L 2 ＝L 1 +[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H 2 ＝H 1 +[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H 2 refers to the height, L, of the rectangle marked by the expanded polygon 2 The length of the rectangle marked by the expanded polygon;
s304, inputting the first label, the second label and the target image into an image processing model to obtain a final text recognition area;
and S305, performing character recognition based on the final text recognition area to obtain a target character string.
The invention has at least the following beneficial effects:
the preset reduction parameters are introduced in the reduction process, so that the length and the width of the target region can be properly reduced in the reduction process, the situation that the text region cannot completely cover the upper boundary and the lower boundary of characters for a rectangle with a large aspect ratio difference in the reduction process is avoided, the preset reduction parameters are introduced, the length and the width of the rectangle are reduced in a self-adaptive mode, and the target characters are finally obtained.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a data processing system for acquiring a text recognition area according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for obtaining an abnormal text recognition area according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for recognizing a target text based on a text according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
A data processing system for acquiring a text recognition area, the system comprising an imaging device, a database in which a specified image list A = { A } is stored, a processor, and a memory in which a computer program is stored 1 ，…，A i ，…，A m }，A i Is the ith designated image, i has a value in the range of 1 to m, m is the number of designated images, and when the computer program is executed by the processor, the following steps are realized:
s101, carrying out affine transformation processing on a target image to obtain an intermediate image corresponding to the target image, wherein the target image is an image corresponding to a target text acquired by a camera device;
wherein, when affine transformation is carried out on the target image, based on the first target point list C = { C = { (C) 1 ，C 2 ，C 3 Acquiring a target point list C by the following steps:
s1011, a first preset point list C ' = { C ' is obtained ' 1 ，…，C′ j ，…，C′ n }，C′ j The number of the jth first preset points is 1 to n, and n is the number of the first preset points, wherein the first type of preset points are pre-designated points and corner points of a target image;
s1013, obtaining C 1 At the fixed area D ζ1 Randomly selecting a first preset at DFixed point, marked as C 2 (ii) a Wherein, C 1 For a randomly selected first preset point, D' means the removal of the fixed zone D ζ1 D = (D) second fixed area list of (c) 1 ,…，D ζ1 ,…，D ζ2 ，…,D ψ )，D ζ The method comprises the steps of indicating a zeta-th fixed area which is divided in a target image, wherein the value range of zeta is from 1 to psi, and psi refers to the number of the fixed areas;
s1015, obtain C 2 Is located in a fixed area D ζ2 ；
S1017, a second preset point list C "= { C" ", corresponding to D", is obtained 1 ，…，C″ j1 ，…，C″ n1 }，C″ j1 The number of the second preset points is j1, the value range of j1 is 1 to n1, n1 is the number of the second preset points, and the second preset points are the first preset points positioned at D'; d' refers to the removal of the fixed region D ζ1 And D ζ2 A third fixed area list of (1);
s1019, go through C', get C ″ j1 And C 1 、C 2 Constitute a first planar area list α "= { α ″" 1 ，…，α″ j1 ，…，α′′ n1 And obtaining a first planar area list S '= { S' = corresponding to the first planar area list 1 ，…，S″ j1 ，…，S″ n1 In which α ″) j1 Means C j1 And C 1 、C 2 Formed flat areas, S j1 Means alpha ″) j1 The corresponding area;
s1021, acquiring a second plane area list S = { S' based on S 1 ，…，S j2 ，…，S n1 And will S 1 The corresponding plane area is taken as a target plane area, and the 3 rd point corresponding to the target plane area is marked as C 3 Wherein S is j2 Refers to the j2 th plane area in the second plane area list, the value range of j2 is 1 to n1, wherein S j2 ≥S j2+1 ；
Based on S1011-S1021, affine transformation is carried out by selecting 3 points of a target image, two points are randomly selected from the pre-designated points and angular points, a fixed area where the two points are located is obtained, the remaining one point in the remaining fixed area is selected to form a plane area, and a third point with the largest plane area is obtained to be used as a target point, so that the situation that three angular points are used in a common method but two or more angular points are true is avoided, and the use condition of affine transformation is expanded; meanwhile, more special points on the target image can be used as target points, so that the identification and acquisition are easier, such as the center point of a preset position area and the center point of an identification code.
S103, a text recognition area list B = { B ] of the intermediate image is acquired 1 ，…，B r ，…，B r1 }，B r ＝(X r ，Y r ，H r ，L r )，B r Is the r-th text recognition area of the intermediate image, and the value range of r is 1 to r 1 ，r 1 Refers to the number of text recognition areas, X r Means B r Coordinates of the upper left-hand corner X-axis, Y r Means B r Coordinates of the upper left-hand Y-axis, H r Means B r Height of (L) r Means B r Of the length of (c).
Specifically, in the present invention, the upper left corner of the target designation image is taken as the origin of coordinate axes, the positive X-axis direction is horizontal to the right, and the positive Y-axis direction is vertical to the bottom.
In one embodiment of the present invention, B is obtained by r ：
S1031, acquiring a target specified image based on the specified image list A and the target image;
s1032, a first history image list B ' = { B ' corresponding to the target specified image is obtained ' 1 ，…，B′ s ,…，B′ s1 S ranges from s to s 1 ，s 1 It is referred to the number of the history images,
s1033, normalizing the first historical image to obtain a second historical image;
s1034, an r text recognition area list B '= { B' of the second history image is obtained 1 ，…，B″ s ,…，B″ s1 }，B″ s ＝(X″ s ，Y″ s ，H″ s ，L″ s )，B″ s Is the r-th text recognition area X' corresponding to the second history image s Is referred to as B ″) s Coordinates of the upper left-hand X-axis, Y ″) s Is referred to as B ″) s Coordinates of the upper left Y-axis, H ″) s Is referred to as B ″) s Height, L ″) s Is referred to as B ″) s Length of (d);
s1035, obtaining B r ：X r ＝X″ s ，Y r ＝Y″ s ，H r ＝max(H″ s )，L r ＝max(L″ s )。
Specifically, before S101, the method further includes:
s1, acquiring a target position corresponding to the intermediate image, identifying based on the target position, and acquiring a target position character string;
s2, traversing the specified image A i A designated position character string corresponding to the target position, and when the designated position character string is equal to the target position character string, a designated image A corresponding to the designated position character string i As a target designation image corresponding to the target image.
Specifically, those skilled in the art know that the position of the target position character string can be obtained through a neural network training method.
Based on S1-S2, the target position character string is identified according to the preset position region, and when the designated title character string is equal to the target title character string, it can be understood that the title of the designated image is the same as the title of the target image, so the designated image is taken as the target designated image corresponding to the target image.
The invention also comprises after S2:
s3, the first target point list C = { C 1 ，C 2 ，C 3 Obtaining a second target point list theta = { theta through affine transformation processing 1 ，θ 2 ，θ 3 }，
S4, obtaining A i ＝{A i1 ，A i2 ，A i3 }，θ＝A i ，A i A list of target points corresponding to the designated image.
Specifically, when the number Sum >1 of target specification images, the following steps are performed:
s31, acquiring a second preset character string list E = { E } corresponding to the target designated image in a second preset position area 1 ，…，E g ，…，E z }，E g The image processing method comprises the steps that a second preset character string corresponding to the g-th target designated image is indicated, the value range of g is 1 to z, and z is the number of the target designated images;
s33, acquiring a second target character string corresponding to the target image in a second preset position area;
and S35, traversing the E to enable the second middle preset character string to be equal to the second target character string, and taking the target designated image corresponding to the second middle preset character string as a final target designated image when the number k' =1 of the second middle preset character string.
In an embodiment of the present invention, the second intermediate predetermined string may be a special field such as "copy".
It can be understood that: when the number of the target designated images is more than 1, a plurality of templates with the same title or templates of the same type belonging to the title are provided, so that when a plurality of target designated images are provided, the final target designated image is determined by comparing the second preset character string, the types of the templates are various, the matched target images are more accurate, meanwhile, the designated position character string and the second middle character string are compared in sequence, and the time is saved and the efficiency is improved.
In another embodiment of the present invention, H can also be obtained in S1035 by the following steps r ：
H r ＝(1/s 1 )∑ s1 s＝1 H″ s 。
In yet another embodiment of the present invention, L can also be obtained in S1035 by the following steps r ：
L r ＝(1/s 1 )∑ s1 s＝1 L″ s 。
Based on S101-S103, affine transformation is carried out on a target image based on three points, so that the direction of the image subjected to affine transformation is completely the same as that of a target designated image, the target image comprises a plurality of text recognition areas, and the length of each text recognition area is determined by obtaining the minimum value and the maximum value of the starting position and the cut-off position of the corresponding text recognition area in S text images.
Example 2
On the basis of the embodiment 1, the invention further comprises a method for identifying based on the abnormal characters, wherein the method comprises the following steps:
s201, when the r text recognition area is not rectangular, based on the CRNN model, obtaining a mapping proportion K 3 A first text recognition result list X = { X ] corresponding to the r-th text recognition area 1 ，…，X k ，…,X k1 }，X k The coordinate of the center of a recognition area corresponding to the kth character on the X axis is defined, and the value range of k is 1 to k 1 ，k 1 The number of characters in the r-th text recognition area is referred to;
s202, identifying a list and mapping proportion K based on the first text 3 Acquiring a second text recognition result list O = { O = { O = 1 ，…，O k ，…,O k1 }，O k ＝(X k1 ，Y k1 ，X k2 ，Y k2 ) Wherein, in the step (A),
X k1 ＝K 3 *X k -H r /2，Y k1 ＝Y″ s ，X k2 ＝K 3 *X k +H r /2，Y k2 ＝Y″ s +H″ s wherein, Y ″) s Refers to the coordinate of the upper left corner Y axis of the r-th text recognition area, H r Identifying a height of the region for the r-th text;
s203, carrying out equal ratio division on the length of the r-th text recognition area based on L ', and acquiring a third text recognition result list O ' = { O ') 1 ，…，O″ k ，…,O″ k1 }，O″ k ＝(X k3 ，Y k3 ，X k4 ，Y k4 )，X k3 Means the coordinate of the upper left corner of the third identification region corresponding to the k character on the X axis, Y k3 Refers to the coordinate, X ', of the upper left corner of the third identification region corresponding to the kth character on the X axis' k2 Means the coordinate of the lower right corner of the third recognition area corresponding to the k character on the X axis, Y k4 Refers to the coordinate of the lower right corner of the third recognition area corresponding to the k-th character on the Y-axis, wherein L' = L r /k 1 ；
S204, when | X k1 -X k3 |＜(Y k2 -Y k1 +Y k4 -Y k3 ) (v 4) set of first position coordinates ((X) k1 +X k3 )/2，(Y k1 +Y k3 )/2，(X k2 +X k4 )/2，(Y k2 +Y k4 ) (v 2) as the kth identification area;
s205, when | X k1 -X k3 |≥(Y k2 -Y k1 +Y k4 -Y k3 ) /4, set the second position coordinate (X) k1 ，Y k1 ，X k2 ，Y k2 ) As the kth recognition region;
s206, acquiring a first heightened identification area based on the k identification area, wherein the first heightened area takes the upper edge of the k identification area as a starting position, increases the height rho in the opposite direction of the Y axis, and takes the length as the character length L r P is a second preset growth factor;
s207, when the first pixel value of the first heightened identification area is larger than the preset pixel value threshold, judging that the second heightened area reaches the sigma-th area 1 Pixel value of heightened region, σ 1 The first pixel value is the average value of the pixel values of all points in the first heightened identification area;
s208, when the average pixel value of the sigma +1 th heightening area is not more than the preset pixel threshold value, acquiring a single character text recognition area corresponding to the sigma heightening area as a final recognition area, wherein the single character text recognition area takes the upper edge of the sigma heightening area as an initial position, and is in the Y-axis direction and highIs H r A rectangle having a width of the length of the third position of the kth character.
Based on S201-S208, when there is an abnormal character, for example, when the character is shifted up and down, the detection using the text recognition area may cause incomplete detection, in the prior art, the range of the text recognition area is often expanded to ensure that all characters are detected, but the detection area is expanded at the same time, and the recognition effect of the up-down tilt of the character is not good, so the invention adopts the second preset growth factor to detect the characters one by one, thereby improving the detection accuracy of a single character.
Example 3
On the basis of the embodiment 2, the invention further comprises a method for recognizing the target text based on the text, wherein the method comprises the following steps:
s301, processing the target image to acquire polygonal labeling information corresponding to the text recognition area;
s302, based on the labeling information of the polygon, the polygon is reduced in height and length to obtain a first label, wherein,
L＝L 1 -[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H＝H 1 -[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 ) Wherein H is 1 Height, L, of the rectangle to which the polygon is labeled 1 The length of a rectangle marked by a polygon is represented, r is an empirical coefficient, H is the height of the rectangle marked by the reduced polygon, L is the length of the rectangle marked by the reduced polygon, and k is a preset reduction parameter;
s303, expanding the outward height and length of the polygon based on the labeling information of the polygon to obtain a second label,
L 2 ＝L 1 +[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H 2 ＝H 1 +[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H 2 is referred to as expandingHeight, L, of rectangle marked by post-tensioned polygon 2 The length of the rectangle marked by the expanded polygon is referred to;
s304, inputting the first label, the second label and the target image into an image processing model to obtain a final text recognition area;
and S305, performing character recognition based on the final text recognition area to obtain a target character string. .
In the prior art, when a DBNet calculates a label, an offset D = a × r/L is calculated, where D is an amount of shortening each side of a labeled polygon, a is an area of the polygon, r is an empirical coefficient 1.5, and L is a length of a side of a multi-deformation, and assuming that our text objects are all rectangles, the formula can be simplified as follows: d = w × h/(w + h), we consider w as a constant and make a partial derivative of h, we get = r (w/(w + h)) × 2, the partial derivative is constantly greater than 0, we increase, let x = w/h get r (x/(x + 1)) × 2, we can find that the partial derivative is incremental, i.e. the partial derivative increases with increasing aspect ratio, i.e. D increases with increasing aspect ratio, and as such, we can create text recognition regions with a larger aspect ratio, whose width after shrank is relatively small, which can prompt the model to learn a region with a relatively narrow width when encountering text regions with a larger aspect ratio, which can prompt the model to output a narrower region, and the text region can not completely cover the upper and lower boundaries of the text.
Based on S301-S305, a text image to be processed is obtained, the text image to be processed is preprocessed, an intermediate text image is obtained, when preprocessing is performed, a label data set is obtained, data enhancement is performed on the label data set, polygon labeling is traversed, when character content exists in a target area, polygon labeling is kept, the target area corresponding to the polygon labeling is obtained, pixels are inwards reduced on each edge of the polygon labeling, preset reduction parameters are introduced in the reduction process, the length and the width of the target area can be properly reduced in the reduction process, the situation that in the reduction process, for a rectangle with a large length-width ratio difference, the text area cannot completely cover the upper boundary and the lower boundary of the character is avoided, the preset reduction parameters are introduced, the length and the width of the rectangle are reduced in a self-adaptive mode, and the target character is finally obtained.
Wherein, after S305, the method further comprises the following steps:
s3051, acquiring a target text recognition area list Q = { Q ] corresponding to the text image to be processed 1 ，…，Q v ，…，Q β Corresponding target text character string, Q, corresponding to the target text recognition area v The method comprises the steps that a v-th target text recognition area corresponding to a text image to be processed is defined, the value range of v is 1 to beta, and beta is the number of the target text recognition areas;
s3053, traversing a target text recognition area list Q according to the r-th text recognition area corresponding to the target designated image corresponding to the text image to be processed to obtain Q v A center point of (a);
s3055, when Q is v When the center point coordinate of (2) belongs to the area range of the r-th text recognition area, the r-th text recognition area and the target text recognition area Q are obtained v The intersection set IoU;
s3057, when IoU is greater than a preset intersection threshold, Q is adjusted v And associating the corresponding target text to the r text recognition area to form a key value pair.
Based on S3051-S3057, when the central point of the target text recognition area is judged to be in the r-th text recognition area corresponding to the target appointed image, acquiring an intersection set of the r-th text recognition area and the target text recognition area, and generating a corresponding key value pair when the intersection set meets a preset intersection range, so that the formed key value pair is more accurately corresponding.
In an embodiment of the present invention, the present invention further includes the following steps:
s10, the method comprises the following steps of, acquiring a predefined feature list U = { U = { (U) } 1 ，…，U γ ，…，U δ }，U γ Refers to the gamma-th feature, gamma ranges from 1 to delta, and delta refers to the predefined number of features.
In one embodiment of the present invention, the predefined feature list includes an identification code, a stamp identifier, a fingerprint identifier, and a signature identifier.
Preferably, δ ≧ 3.
S20, detecting whether the text image to be processed comprises the predefined characteristics.
S30, when the text image to be processed has U γ When in use, U is turned on γ The corresponding key-value pair is labeled "1".
S40, when the text image to be processed does not have U γ In time, U will be turned γ The corresponding key-value pair is labeled "0".
Further, the key-value pair identifier is used for identifying whether the feature has an identifier of the text image to be processed, wherein the key-value pair identifier is "1" or "0"; it can be understood that: those skilled in the art know that when a feature exists in the present image to be processed, the key-value pair is identified as "1" or "0"; otherwise, when the feature does not have a text image to be processed, the key-value pair is identified as "0" or "1".
Preferably, when the characteristic exists in the to-be-processed text image, the key-value pair is marked as "1"; otherwise, when the feature does not have a text image to be processed, the key-value pair is identified as "0".
And based on S10-S40, judging whether the to-be-processed text image comprises the characteristics such as the identification code and the like by using the universal target detection, and detecting the characteristics in the to-be-processed text image.
Embodiments of the present invention also provide a non-transitory computer-readable storage medium, which may be disposed in an electronic device to store at least one instruction or at least one program for implementing a method of the method embodiments, where the at least one instruction or the at least one program is loaded into and executed by a processor to implement the method provided by the above embodiments.
Embodiments of the present invention also provide an electronic device comprising a processor and the aforementioned non-transitory computer-readable storage medium.
Embodiments of the present invention also provide a computer program product comprising program code means for causing an electronic device to carry out the steps of the method according to various exemplary embodiments of the invention described above in the present description, when said program product is run on the electronic device.
Although some specific embodiments of the present invention have been described in detail by way of illustration, it should be understood by those skilled in the art that the above illustration is only for the purpose of illustration and is not intended to limit the scope of the invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. A method for recognizing a target text based on a text, the method comprising the steps of:
s301, processing the target image to acquire polygonal labeling information corresponding to the text recognition area;
s302, based on the labeling information of the polygon, the polygon is reduced in height and length to obtain a first label, wherein,
L＝L 1 -[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H＝H 1 -[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 ) Wherein H is 1 Height, L, of the rectangle to which the polygon is labeled 1 The length of a rectangle marked by a polygon is represented, r is an empirical coefficient, H is the height of the rectangle marked by the reduced polygon, L is the length of the rectangle marked by the reduced polygon, and k is a preset reduction parameter;
s303, expanding the outward height and length of the polygon based on the labeling information of the polygon to obtain a second label,
L 2 ＝L 1 +[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H 2 ＝H 1 +[L 1 *H 1 *r/(L 1 +H 1 )]*(1-k*L 1 /H 1 )，H 2 refers to the height, L, of the rectangle of the expanded polygon 2 Referred to as rectangles marked by expanded polygonsA length;
s304, inputting the first label, the second label and the target image into an image processing model to obtain a final text recognition area;
and S305, performing character recognition based on the final text recognition area to obtain a target character string.
2. The method of claim 1, wherein r is 1.5.
3. The method of claim 1, wherein k is 0.05.
4. The method for recognizing a target text based on a text as claimed in claim 1, further comprising the following steps after S305:
s3051, acquiring a target text recognition area list Q = { Q ] corresponding to the text image to be processed 1 ，…，Q v ，…，Q β Corresponding target text character string, Q, corresponding to the target text recognition area v The method comprises the steps of indicating a v-th target text recognition area corresponding to a text image to be processed, wherein the value range of v is from 1 to beta, and beta is the number of the target text recognition areas;
s3053, traversing a target text recognition area list Q according to an r-th text recognition area corresponding to a target designated image corresponding to a text image to be processed, and acquiring Q v A center point of (a);
s3055, when Q is v When the center point coordinate of (2) belongs to the area range of the r-th text recognition area, the r-th text recognition area and the target text recognition area Q are obtained v The intersection set IoU;
s3057, when IoU is greater than a preset intersection threshold, Q is adjusted v And associating the corresponding target text to the r text recognition area to form a key value pair.
5. The method of claim 4, wherein the target specific image and the text image to be processed are in the same coordinate system.
6. The method for recognizing a target text based on a text according to claim 1, further comprising the steps of:
s10, the method comprises the following steps of, acquiring a predefined feature list U = { U = { (U) } 1 ，…，U γ ，…，U δ }，U γ The method is characterized in that the method refers to the gamma-th characteristic, the value range of gamma is 1 to delta, and delta refers to the predefined characteristic quantity;
s20, detecting whether the text image to be processed comprises predefined characteristics;
s30, when the text image to be processed has U γ When in use, U is turned on γ The corresponding key-value pair is labeled "1";
s40, when the text image to be processed does not have U γ When in use, U is turned on γ The corresponding key-value pair is labeled "0".
7. The method of claim 6, wherein the predefined list of features includes an identification code.
8. The method of claim 6, wherein δ ≧ 3.
9. A non-transitory computer readable storage medium having stored therein at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the method of any one of claims 1-8.
10. An electronic device comprising a processor and the non-transitory computer readable storage medium of claim 9.
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