CN110009065A - A kind of calligraphy comparison method based on image binaryzation - Google Patents
A kind of calligraphy comparison method based on image binaryzation Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/24—Character recognition characterised by the processing or recognition method
- G06V30/242—Division of the character sequences into groups prior to recognition; Selection of dictionaries
- G06V30/244—Division of the character sequences into groups prior to recognition; Selection of dictionaries using graphical properties, e.g. alphabet type or font
- G06V30/245—Font recognition
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Abstract
The invention discloses a kind of calligraphy comparison method based on image binaryzation, which is characterized in that this method comprises: step 1 is to handle the calligraphy picture image binaryzation principle of drills to improve one's handwriting person for binary picture, be set as figure a；Step 2 is to handle the picture image binaryzation principle of copybook as binary picture, is set as figure b；Step 3 is that the figure a that step 1 processing obtains and the figure b that step 2 processing obtains are carried out picture size processing, and the size of two figures is adjusted to consistent；Step 4 is comparative selection direction, judges that selection emphasizes Hanzi structure comparison or emphasizes that Chinese-character stroke compares；Step 5 is to compare effect to show；Step 6 is the advisory opinion that practitioner's practice is provided by deep learning.The comparing result of image, text or number that calligraphy comparison method of the invention provides is very intuitive, can be convenient practitioner and realizes the font of oneself and the difference of copybook, provides the advisory opinion that practitioner practices calligraphy.
Description
Technical field
The present invention relates to calligraphy education field, specifically a kind of calligraphy comparison method based on image binaryzation.
Background technique
Chinese character is one of text most ancient in the world, at least thousands of years of history, and the original character of earliest extant is
The stone inscription character of ancient times, the mature hanzi system that can be known are the inscriptions on bones or tortoise shells of Shang dynasty.Chinese character is physically gradually being become by figure
For stroke, pictograph becomes signifying, complexity becomes simple；In coinage in principle from table shape, express the meaning to ideophone.The other example of depolarization
It outside, is all one syllable of a Chinese character.
The calligraphy art of China starts from the generation stage of Chinese character, and " sound cannot be passed in strange land, be stayed in the different time, as a result literary
Word is raw.Text person, so for the mark of meaning and sound." therefore, produce text.First works of calligraphy art are not texts,
But some portray symbol -- pictograph or picture writing；Chinese character portrays symbol, primarily occur ins on pottery.Initial
Portraying symbol only indicates the concept of a general chaos, without exact meaning.
Retrieval is for the available following nearlyr search result of method that calligraphy compares:
State Intellectual Property Office discloses the patent document of Publication No. CN 102147867A, a kind of state based on main body
The recognition methods of picture picture and handwriting image comprising following steps: (1) the pervious state of scanner scanning China modern is utilized
Paintings product and the calligraphy work occurred in history, obtain the sample image of traditional Chinese Painting works and calligraphy work；(2) to sample graph
As carrying out the image preprocessing based on Top-down comprising following steps: 1. converting sample image from RGB color
To hsv color space；2. doing the edge detection of Canny operator to the sample image in 1. hsv color space that step obtains；③
The processing of edge swell is done to the sample image of 2. edge detection that step obtains；4. the edge swell 3. obtained to step
Sample image does the processing of area filling；5. other than the filling region of the sample image of the area filling 4. obtained to step
The statistics that background area carries out colouring information obtains each point including counting to each component in hsv color space
Average value Ave_H, Ave_S, Ave_V of amount；6. carrying out time of full figure individual element to the sample image that preliminary sweep obtains
Go through, the value of each component of H, S, V of the HSV color space of each pixel of sample image respectively with it is each in hsv color space
Average value Ave_H, Ave_S, Ave_V of component do difference operation, difference operation result are compared with threshold value, in threshold value
Pixel in range is considered to be left white region, sets it to uniform color；(3) from the obtained sample image of scanning with
Machine chooses training sample image and test sample image；(4) main body that training sample image is extracted from training sample image is special
Levy vector, and training classifier comprising following steps: 1. obtain training sample image by step (2) image preprocessing
Grey level histogram, gray scale are 256 ranks；2. being counted for each color interval bin in training sample image grey level histogram
The number summation Total occurred in training sample image, each training sample image ultimately produce the main body of one 256 dimension
Feature vector completes the extraction of the body feature vector of training sample image, it is contemplated that the size of different training sample images,
It is calculated using the following equation: Total_bin=Total Wide × High, wherein Wide, High respectively indicate training sample figure
The width and height of picture；(5) training sample image classifier extracts body feature vector, using training from test sample image
Sample image classifier identified comprising following steps: 1. to the body feature for the training sample image extracted to
Amount, is trained based on machine learning model, obtains trained sample image classifier；2. extracting test sample image
Body feature vector；3. the body feature vector of the test sample image for extraction is classified with trained sample image
Device is identified, obtains the result of identification.
State Intellectual Property Office discloses the patent document of Publication No. CN107491543A for another example, and one kind being based on client
The calligraphy auxiliary exercise method at end characterized by comprising when the search information that client monitors to user input, send
Searching request；Described search information includes keyword or the pass of word content, the font of the word content and the word or poetic prose
Key sentence；Receive based on described search request be the word content return the evolution of the word, the comparison between multiple fonts and
The difference of order of writing strokes between the multiple fonts；Receive the word based on described search request for the word content and the word
The order of strokes observed in calligraphy path animation of the word that body returns, stroke split after each stroke picture and/or the word knowledge point；It connects
Receive the pen and ink picture and/or the poetic prose of the calligraphist returned based on the keyword or critical sentence that described search request is poetic prose
In each word search link, described search be linked as be with the font of the word content and the word search information search,
Or with the word content be search for information search；The content of aforementioned return is shown in the client.
By retrieving existing open source literature it can be found that existing calligraphy auxiliary exercise method is in the prevalence of system
Structure is complicated, safety is low, and the not mature enough stabilization of the core algorithm used, reliability are low, and comparing result is not intuitive enough, practice
Person cannot obtain clear direct practice advisory opinion.
Summary of the invention
A kind of utilization image binaryzation principle processing binaryzation that in order to overcome above-mentioned deficiency, present invention aims at providing
Figure come reach calligraphy compare purpose method.
To achieve the goals above, the technical solution adopted by the present invention is that:
Include: step 1 it is to handle the calligraphy picture image binaryzation principle of drills to improve one's handwriting person as binary picture, is set as
Scheme a；Step 2 is to handle the picture image binaryzation principle of copybook as binary picture, is set as figure b；Step 3 is by step 1
It handles obtained figure a and step 2 handles obtained figure b and carries out picture size processing, the size of two figures is adjusted to one
It causes；Step 4 is comparative selection direction, judges that selection emphasizes Hanzi structure comparison or emphasizes that Chinese-character stroke compares；Step 5 be than
Effect is shown；Step 6 is the advisory opinion that practitioner's practice is provided by deep learning；
Step 1: the calligraphy picture image binaryzation principle of drills to improve one's handwriting person being handled as binary picture, figure a is set as
The calligraphy picture of drills to improve one's handwriting person image binaryzation principle is handled, which uses OTSU algorithm, if
Image includes L gray level, L 0,1 ..., L-1；The pixel points that gray value is i are Ni, and total pixel points of image are N
=N0+N1+...+N (L-1)；
Gray value is the point of i are as follows:
P (i)=N (i)/N；
Entire image is divided into two class of dark space c1 and clear zone c2 by thresholding t, and inter-class variance σ is the function of t:
σ=a1*a2 (u1-u2) ^2 (2)；
In formula, aj is the ratio between area and total image area of class cj, a1=sum (P (i))；I- > t, a2=1-a1；Uj is
The mean value of class cj, u1=sum (i*P (i))/a1；0->t；
U2=sum (i*P (i))/a2, t+1- > L-1；
Method selection optimum thresholding t^ keeps inter-class variance maximum, it may be assumed that enables Δ u=u1-u2, σ b=max { a1 (t) * a2 (t)
Δu^2}；
Obtain a result Px；
Step 2: the picture image binaryzation principle of copybook being handled as binary picture, figure b is set as
The picture of copybook image binaryzation principle is handled, which obtains knot with step 1 for OTSU algorithm
Fruit is set as P；
Step 3: the figure a that step 1 processing obtains and the figure b that step 2 processing obtains being subjected to picture size processing, by two figures
Gear size be adjusted to consistent
The figure a that step 1 processing obtains and the figure b that step 2 processing obtains are subjected to picture size processing, by the size of two figures
Size is adjusted to unanimously, to adjust so that Size (A)=Size (B)；
Step 4: comparative selection/comparison direction judges that selection emphasizes Hanzi structure comparison or emphasizes that Chinese-character stroke compares
The stroke number of current Chinese character is set as e；
When the comparison process selection emphasize Hanzi structure compare, then the knot of main stroke is selected in picture a and picture b
Structure position is chosen e point and is compared, and finds out Px the and P value of the position to compare；
When the stroke contrast of Chinese character is emphasized in comparison process selection, then each stroke is selected in picture a and picture b
The receipts position at initial position and stroke end compares, and finds out Px the and P value of the position to compare；
Step 5: comparing effect and show
Px obtained in step 3 and step 4 and P are made comparisons, the difference of the two is obtained using ghost image control methods, will
Differential disply out comes out；
Step 6: the advisory opinion of practitioner's practice is provided by deep learning
The advisory opinion of practitioner's practice is provided by deep learning.
Further, step 5 compares effect and both of shows that difference can be directly displayed out or be used by image
Data either character express comes out.
Beneficial effects of the present invention:
Since comparing result can intuitively be shown with image, text or number, practitioner is facilitated to realize oneself
Font and font specific difference, and based on deep learning can provide practitioner practice advisory opinion；
Due to mature and stable, the of the invention high reliablity of the core algorithm Image binarizing algorithm used；
Since system structure is simple, safety is good.
Detailed description of the invention
The present invention is described in further detail with reference to the accompanying drawing.
Fig. 1 is main-process stream schematic diagram of the invention.
Specific embodiment
Present invention is described in the following with reference to the drawings and specific embodiments, to better understand the present invention.
As shown in Figure 1, being preceding 5 steps of a total of six step of the present invention in figure, step 1 is by the book of drills to improve one's handwriting person
The processing of method picture image binaryzation principle is binary picture, is set as figure a；Step 2 is by the picture image binaryzation of copybook
Principle processing is binary picture, is set as figure b；Step 3 be the step 1 obtained figure a of processing and step 2 are handled obtained figure b into
The processing of row picture size, is adjusted to consistent for the size of two figures；Step 4 is comparative selection direction, judges that the Chinese is emphasized in selection
Word Structure Comparison emphasizes that Chinese-character stroke compares；Step 5 is to compare effect to show；Step 6 is to provide practice by deep learning
The advisory opinion of person's practice；Wherein step 1 is to handle the calligraphy picture image binaryzation principle of drills to improve one's handwriting person for two-value
Change figure, be set as figure a, which handles the calligraphy picture of drills to improve one's handwriting person image binaryzation principle, which uses
OTSU algorithm, if image includes L gray level, L 0,1 ..., L-1；Pixel that gray value is i points are Ni, image always as
Vegetarian refreshments number is N=N0+N1+...+N (L-1)；Gray value is the point of i are as follows: P (i)=N (i)/N；Entire image is divided by thresholding t
Two class of dark space c1 and clear zone c2, inter-class variance σ are the functions of t: σ=a1*a2 (u1-u2) ^2 (2)；In formula, aj is class cj's
The ratio between area and total image area, a1=sum (P (i))；I- > t, a2=1-a1；Uj is the mean value of class cj, u1=sum (i*P
(i))/a1；0->t；U2=sum (i*P (i))/a2, t+1- > L-1；Method selection optimum thresholding t^ keeps inter-class variance maximum,
That is: Δ u=u1-u2, σ b=max { a1 (t) * a2 (t) Δ u^2 } are enabled；Obtain a result Px；Step 2 is to scheme the picture of copybook
It is binary picture as binaryzation principle is handled, is set as figure b；The step handles the picture of copybook image binaryzation principle, should
Processing method is that OTSU algorithm obtains a result with step 1 and is set as P；Step 3 is the figure a for obtaining step 1 processing and step 2
It handles obtained figure b and carries out picture size processing, the gear size of two figures is adjusted to consistent；The step handles step 1
To figure a and the obtained figure b of step 2 processing carry out picture size processing, the size of two figures is adjusted to unanimously, to adjust
So that Size (A)=Size (B)；Step 4 is comparative selection/comparison direction, judges that selection is emphasized Hanzi structure comparison or emphasized
Chinese-character stroke compares the step and the stroke number of current Chinese character is set as e；User can choose way of contrast, user's selection at this time
Way of contrast will determine the emphasis of the comparison process；When comparison process selection emphasizes that Hanzi structure compares, then in picture
It selects the locations of structures of main stroke to choose e point in a and picture b to compare, finds out Px the and P value of the position to compare；When
The comparison process selects to emphasize the stroke contrast of Chinese character, then the initial position of each stroke is selected in picture a and picture b
It is compared with the receipts position at stroke end, finds out Px the and P value of the position to compare；Step 5 is to compare effect to show；
The step makes comparisons Px obtained in step 3 and step 4 and P, and the difference of the two is obtained using ghost image control methods, will
Differential disply out comes out；Effect is compared at this time both of shows that difference can be directly displayed out or be used by image
Data either character express comes out.
Step 6 is the advisory opinion that practitioner's practice is provided by deep learning；The step provides white silk by deep learning
The advisory opinion of habit person's practice, since current deep learning theory is very abundant, deep learning is led in natural language processing etc.
Domain is mainly used in machine translation and semantic excavation, and is used to can be used when the practising Chinese character calligraphy in China and for example pass through
Pytorch or tensorflow deep learning framework builds a Chinese character calligraphy writing Character Font Recognition and the font, and some is specific
The automatic recommended models of the literary style of word provide most appropriate practice suggestion to help the final step of the calligraphy comparison method；Than
The Chinese character calligraphy established such as the deep learning framework using pytorch writes font identification model and recognizes certain user
Some Chinese-character writing be Ouyang Xun Ouyang Style word in " hut " word, then according to user in step 3 and step 4 of the invention
Specific difference caused by middle comparison Px and P provides in Ouyang Style the suggestion of this kind of mistake about this kind of word: " should keep away just keep away it is close just
Dredge, hedging is just easy, keep away it is remote nearby, be intended to its enhance each other's beauty each other it is proper ", " hut word, upper one skims both sharp, and next slash is improper identical, should keep away
Overlapping and just simple diameter ", then by the current mistake of record user, this mistake is combined in the similar font of lower secondary design
Degree provides picture, number and the text importing and further suggest whether the corresponding similar mistake of user is promoted.
Claims (2)
1. a kind of calligraphy comparison method based on image binaryzation, which is characterized in that this method comprises: step 1 is to practice calligraphy
The calligraphy picture image binaryzation principle processing of habit person is binary picture, is set as figure a；Step 2 is to scheme the picture of copybook
It is binary picture as binaryzation principle is handled, is set as figure b；Step 3 is to obtain the figure a that step 1 processing obtains with step 2 processing
Figure b carry out picture size processing, the size of two figures is adjusted to consistent；Step 4 is comparative selection direction, judges to select
It emphasizes Hanzi structure comparison or emphasizes that Chinese-character stroke compares；Step 5 is to compare effect to show；Step 6 is to be given by deep learning
The advisory opinion of practitioner's practice out；
Step 1: the calligraphy picture image binaryzation principle of drills to improve one's handwriting person being handled as binary picture, figure a is set as
The calligraphy picture of drills to improve one's handwriting person image binaryzation principle is handled, which uses OTSU algorithm, if image
Include L gray level, L 0,1 ..., L-1；The pixel points that gray value is i are Ni, and total pixel points of image are N=N0+N1
+...+N(L-1)；
Gray value is the point of i are as follows:
P (i)=N (i)/N；
Entire image is divided into two class of dark space c1 and clear zone c2 by thresholding t, and inter-class variance σ is the function of t:
σ=a1*a2 (u1-u2) ^2 (2)；
In formula, aj is the ratio between area and total image area of class cj, a1=sum (P (i))；I- > t, a2=1-a1；Uj is class cj
Mean value, u1=sum (i*P (i))/a1；0->t；
U2=sum (i*P (i))/a2, t+1- > L-1；
Method selection optimum thresholding t^ keeps inter-class variance maximum, it may be assumed that enables Δ u=u1-u2, σ b=max { a1 (t) * a2 (t) Δ u^
2}；
Obtain a result Px；
Step 2: the picture image binaryzation principle of copybook being handled as binary picture, figure b is set as
The picture of copybook image binaryzation principle is handled, which is that OTSU algorithm is obtained a result and set with step 1
For P；
Step 3: the figure a that step 1 processing obtains and the figure b that step 2 processing obtains being subjected to picture size processing, by the tooth of two figures
Wheel size is adjusted to consistent
The figure a that step 1 processing obtains and the figure b that step 2 processing obtains are subjected to picture size processing, by the size of two figures
It is adjusted to consistent, adjusts so that Size (A)=Size (B)；
Step 4: comparative selection/comparison direction judges that selection emphasizes Hanzi structure comparison or emphasizes that Chinese-character stroke compares
The stroke number of current Chinese character is set as e；
When the comparison process selection emphasize Hanzi structure compare, then the structure bit of main stroke is selected in picture a and picture b
It sets selection e point to compare, finds out Px the and P value of the position to compare；
When the stroke contrast of Chinese character is emphasized in comparison process selection, then the starting of each stroke is selected in picture a and picture b
The receipts position at position and stroke end compares, and finds out Px the and P value of the position to compare；
Step 5: comparing effect and show
Px obtained in step 3 and step 4 and P are made comparisons, the difference of the two is obtained using ghost image control methods, by what is obtained
Differential disply comes out；
Step 6: the advisory opinion of practitioner's practice is provided by deep learning
The advisory opinion of practitioner's practice is provided by deep learning.
2. the calligraphy comparison method according to claim 1 based on image binaryzation, it is characterised in that: step 5 compares effect
Fruit both of shows that difference can be by including that image, data, text are directly stated and come out.
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Cited By (1)
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| CN112800936A (en) * | 2021-01-25 | 2021-05-14 | 中南大学 | Calligraphy copy intelligent evaluation and guidance method based on computer vision |
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