CN100583135C - Computer estimation method of Chinese character writing shape beauty degree - Google Patents

Abstract

The invention discloses a computer assessment method of aesthetic degree of writing morphology of Chinese characters. The method is obtained by machine learning based on the scoring of the aesthetic degree of an artificially given writing sample of Chinese characters. The decomposition of strokes and the parameterization of the writing morphology of Chinese characters are firstly carried out, and then the potential relationship between the writing morphology of Chinese characters and the aesthetic degree is obtained by using the image processing and the artificial intelligent method. The user intelligent interaction is introduced during the process of the decomposition of strokes and the parameterization in the first step, thus improving the processing ability of illegible handwriting. The computer assessment method respectively carries out the scoring of the aesthetic degree of each individual stroke in the writing morphology of Chinese characters, the spatial structure in all parts of the character form and the consistency of the writing style of all parts of the character form during the process in the second step of learning the relationship between the writing morphology and the artificial scoring; and finally the score of the overall aesthetic degree of the writing morphology of Chinese characters is given out by integrating the scoring results. The method has the advantage that the method can be automatically implemented by a computer.

Description

A kind of computer estimation method of Chinese character writing morphology beauty

Technical field

The present invention relates to computer art and aesthetics and artificial intelligence field, relate in particular to a kind of computer estimation method of Chinese character aesthetics.

Background technology

The art thinking that has had a large amount of work to come simulating human, and further set up the computer intelligence system to solve the problem in the real world.Aspect Chinese words, Proceedings of theInternational Conference on Computer Processing of Oriental Languages (ICCPOL) proceeding of nineteen ninety-five (article title " Chinese glyph generation using character composition andbeauty evaluation metrics ") has been announced a problem of using didactic method to attempt qualitative assessment Chinese font aesthetic feeling: they have defined four rules in writing of Chinese character, and have been implemented in their the rule-based aesthstic grading module; This module is calculated corresponding mark one by one to four rules simply, and obtains their weighted sum.IEEE Intelligent Systems magazine in 2005 (article title " Automaticgeneration of artistic Chinese calligraphy " is hereinafter to be referred as document IS2005) has been published the automatic creation system of a Chinese art calligraphy.But their work mainly focuses on use and generates formative Chinese font based on the reasoning that retrains, and how not to have aesthetic feeling and almost be concerned about these generations result.

Generate the result in order to obtain better computing machine Chinese font, also aesthetics is done quantitative Analysis, thereby we have realized the scoring of Chinese character aesthetics by learning basic numerical relation training set behind in order to attempt.Manyly used the people of expert system to know at work, senior Expert Rules can operate as normal; And this might not be the knowledge blind spot owing to expert system itself sometimes, and perhaps problem can't be summed up at all.Therefore we think that we can provide a kind of brain than the human expert to evaluate and test better machine evaluating ability based on the data-driven method of learning art.

Aspect drawing, the work that has some to study automatic painting creation equally in the field of Computer Graphics, but this mostly is to finish on the basis of a given photos.Other also the someone explored the animation of creating the drawing style in conjunction with artificial intelligence and human-computer interaction technology, the method for decomposing with stroke as the article " Animating Chinese paintings through stroke-based decomposition " of ACM journal ACM Trans.Graph publication in 2006 realizes dynamic drawing.Outside the visual art field, Computer Music is the successful direction that creation is carried out or assisted to the Another application artificial intelligence technology.In international artificial intelligence associating conference (IJCAI2007) in 2007, have one independently the special topic make music artificial intelligence (MUSIC-AI2007) discuss this topic specially.It should be noted that the research for Computer Music comprises automatic music creation and music evaluation, this is more similar to our thinking on Chinese font.Also have other number of research projects in addition: as the story creation, credible law enforcement official, interactive story, or the like, all be intended to catch aesthstic calculability.

Summary of the invention

The computer estimation method that the purpose of this invention is to provide a kind of Chinese character aesthetics, with a mechanism based on machine learning algorithm, at first a part is learnt by the Hanzi specimen font of the given aesthetics scoring of human judging panel, analyze the parameterized information of each font, use the method for Flame Image Process and artificial intelligence to obtain potential relation between type posture and its aesthetics then, and then can do scoring the aesthetics of Chinese character style.

The computer estimation method of Chinese character aesthetics comprises the steps:

1) mode by many people's investigation is the stroke sample scoring that 500～2000 Chinese character input single strokes are drawn, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font one stroke form and its aesthetics evaluation result, uses the learner after the training that font one stroke aesthetics is marked then;

2) mode by many people's investigation is the inner structure scoring of 500～2000 Hanzi specimens, and end user's artificial neural networks, decision tree, the mapping relations between font inner structural features and its aesthetics evaluation result are trained and obtained to fuzzy logic or support vector machine, use the learner after training that the font inner structure is marked then, wherein, the font inner structure comprises the topological relation between each inner stroke of font or radical element, relative position relation, area hides relation, and stroke and stroke, the difference of the spatial relation between stroke and the radical and the standard letter of this word realizes the aesthetics scoring that the each several part space structure in the font is distributed;

3) mode by many people's investigation is the style consistance scoring of 100～300 Hanzi specimens, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font style consistance feature and its aesthetics evaluation result, uses the learner after the training that the font style consistance is marked; Each inner stroke of font or radical element are calculated similarity with the multiple known style font of this word, and determine style consistance scoring of this font with this;

4) according to the one stroke aesthetics scoring that Chinese character is carried out, the scoring of inner structure aesthetics, the scoring of style consistance, the comprehensive every score of applied statistics learning method obtains the overall aesthetic degree scoring of this font.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that described mode by many people's investigation is the stroke sample scoring that 500～2000 Chinese character input single strokes are drawn, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font one stroke form and its aesthetics evaluation result, uses learner after the training that font one stroke aesthetics is carried out methods of marking then and comprise the steps:

A) in advance by many people investigation method, allow six people separately the one stroke image from 100 Chinese characters be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this stroke, that is: add up respectively the number of stroke scoring for " good ", " generally ", " poor ", divided by total number of persons, three percentages of gained are respectively the probability of this stroke aesthetics for " good ", " generally ", " poor ", obtain the artificial evaluation result of the aesthetics of 500～2000 one stroke;

B) with the regular script volume image of each stroke described in the step a) standard stroke as this stroke; With these one stroke image parameterizations, promptly extract their profile, track, position signalling, convert the form of vector to, these vectors are carried out pre-service, remove noise, and further extract proper vector, to the proper vector that obtains, make vector subtraction with the proper vector of the standard stroke of this stroke, thereby obtain the morphological differences vector of one stroke;

C) signal difference and the relation of the Function Mapping between the aesthetics evaluation result in the morphological differences vector of one stroke trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; Promptly with morphological differences vector and aesthetics evaluation result respectively as the input and output of artificial neural network, decision tree, fuzzy logic or support vector machine; The artificial evaluation result of applying step in a) implemented the machine learning process that has feedback to artificial neural network or decision tree or fuzzy logic or support vector machine;

D) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that one stroke is marked: promptly to any given one stroke image, applying step b) obtains morphological differences vector between this stroke and its standard stroke, with this vector as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the aesthetics score of this Chinese character stroke.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that the inner structure scoring that described mode by many people's investigation is 500～2000 Hanzi specimens, and end user's artificial neural networks, decision tree, the mapping relations between font inner structural features and its aesthetics evaluation result are trained and obtained to fuzzy logic or support vector machine, use the learner after training that the font inner structure is marked then, wherein, the font inner structure comprises the topological relation between each inner stroke of font or radical element, relative position relation, area hides relation, and stroke and stroke, the difference of the spatial relation between stroke and the radical and the standard letter of this word, realization may further comprise the steps the aesthetics methods of marking that the each several part space structure in the font distributes:

E) in advance by many people investigation method, in advance by many people investigation method, allow six people respectively Chinese character image be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively the number of font scoring for " good ", " generally ", " poor ", divided by total number of persons, gained percentage is respectively the probability of this font mark for " good ", " generally ", " poor "; Obtain the artificial evaluation result of 500～2000 font space structure aesthetics by such mode;

F) by the stroke decomposition result with this font parameterization, convert to the vector form; The expression of these vectorial stratification the profile of font, track and relative position information;

G) with the regular script volume image of each font described in the step e) standard letter as this font; Extract in these corresponding vectors of font institute, the topology between each stroke or radical, the signal of geometric relationship compare with the topology of its standard letter, the signal of geometric relationship, obtain its signal difference, promptly do vector subtraction;

H) mapping relations between vector signal difference and aesthetics evaluation result are trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; Applying step e) artificial evaluation result of demarcating in is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine.

I) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that font space structure aesthetics is marked.Promptly to any given font image, applying step f)-step g) obtains the signal difference between the standard letter of this font inner structure and this word, with this difference vector as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the inner structure aesthetics score of this font.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that the style consistance scoring that described mode by many people's investigation is 100～300 Hanzi specimens, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font style consistance feature and its aesthetics evaluation result, uses the learner after the training that the font style consistance is marked; Each inner stroke of font or radical element are calculated similarity with the multiple known style font of this word, and comprise the steps: with this method of determining the style consistance scoring of this font

J) in advance by many people investigation method, allow a plurality of people respectively the style consistance of a plurality of Chinese character images be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: respectively statistics with the number of this word scoring for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this this word mark is " good ", " generally ", " poor "; Obtain 100～300 conforming artificial evaluation results of Chinese character style style by such mode;

K) be ready to write the form sample in regular script body, running hand body and the Li Shu Ti of these Chinese character correspondences, or more other fonts, as known font;

L) pass through the stroke decomposition result with above font parameterization, convert the form of vector to; The expression of these vectorial stratification the profile of font, track and relative position information;

M), calculate the similarity degree of this part various known these parts of font with it to each part of each font; It is one of following two kinds that the computing method of the similarity Sij of one stroke Ai and known font Bj can be chosen wantonly:

(1) with the standard stroke of known font Bj as one stroke Ai, use one stroke aesthetics methods of marking calculates three probable value P1 of this stroke aesthetics, P2, and P3 represents the probability of this stroke aesthetics for " good ", " generally ", " poor " respectively; In the present embodiment, Sij=P1 * 50%+P2 * 50%;

(2) obtain the scope rectangle of stroke Ai, can comprise the minimum rectangle in the rectangle of these all parts of stroke, and by continuous translation with around scope rectangular centre rotation Ai, the writing that makes Ai and the writing of the corresponding stroke of its font Bj overlap the area maximum; If the writing area of Ai is C1, the area of the writing of the corresponding stroke of its font Bj is C2, then Sij=|C1 ∩ C2|/| C1 ∪ C2|, and similarity degree is the ratio of C1, C2 common factor area and C1, C2 union area;

N) the similarity degree polymerization with each part of font and every kind of known font can obtain a matrix, as the style consistance feature of this font;

O) mapping relations between this matrix and aesthetics evaluation result are trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

P) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the font style consistance is marked.Promptly to any given font image, applying step k)-and step n) obtain the similarity between this font and each known font, with this matrix as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the style consistance score of this font.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that the scoring of one stroke aesthetics, the scoring of inner structure aesthetics, the scoring of style consistance that described basis is carried out Chinese character, the comprehensive every score of applied statistics learning method, the method that obtains the overall aesthetic degree scoring of this font comprises the steps:

Q) utilize above-mentioned three methods, use automatic each one stroke aesthetics score, inner structure aesthetics score and the font style consistance score that obtains 100～300 Chinese characters of artificial neural network, decision tree, fuzzy logic or support vector machine after training;

R), allow a plurality of people respectively to step q by many people investigation method) described in Chinese character image do the aesthetics TOP SCORES, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor ";

S) end user's artificial neural networks, decision tree, fuzzy logic or support vector machine are trained and are obtained step q) in score and step r) in the aesthetics TOP SCORES between mapping relations; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

T) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the overall aesthetic degree of font is marked, promptly to any given Chinese character style image, applying step q) method obtains one stroke aesthetics score, inner structure aesthetics score and the font style consistance score of this font, and with these scores as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the overall aesthetic degree score of this font.

The beneficial effect that the present invention compared with prior art has:

(1) combines kinds of artificial intelligence and image processing techniques, make computing machine become possibility the aesthetics evaluation of Chinese character;

(2) utilized a kind of mechanism of machine learning, made that computing machine can be from the mankind's aesthetic conceptions study to the aesthetics evaluating ability of Chinese character;

(3) in the stroke of Chinese character is decomposed, introduced user's interactive mode input, improve the serious especially cursive stroke of font of deformation greatly and decomposed effect.

Description of drawings

Fig. 1 is the process flow diagram of embodiment of the present invention system;

Fig. 2 is the synoptic diagram of the each several part scoring algorithm of aesthetics scoring algorithm of the present invention;

Fig. 3 (a) is the Hanzi specimen font;

Fig. 3 (b) is the refinement result of font among Fig. 3 (a);

Fig. 3 (c) is " geometric graph " of font among Fig. 3 (a);

Fig. 4 is that stroke of the present invention is decomposed and the parameterized flow example figure of Chinese character;

Fig. 4 (a) is the Hanzi specimen font;

Fig. 4 (b) is " geometric graph " of Fig. 4 (a);

Fig. 4 (c) is the corresponding standard letter of Fig. 4 (a), i.e. round hand;

Fig. 4 (d) is the stroke decomposition result of Fig. 4 (a) on skeleton;

Fig. 4 (e) is the final stroke decomposition result of Fig. 4 (a);

Fig. 5 is that the User Interface that utilizes of the present invention assists stroke to decompose and the parameterized flow example figure of Chinese character;

Fig. 5 (a) is the Hanzi specimen font;

Fig. 5 (b) is " geometric graph " of Fig. 5 (a);

Fig. 5 (c) is the standard letter of Fig. 5 (a), i.e. round hand;

Fig. 5 (d) is the automatic decomposition result of Fig. 5 (a), and colored stroke is illustrated in the stroke of automatic decomposition success;

The residue stroke sketch that Fig. 5 (e) sketches the contours on font by interactive interface for the user;

Fig. 5 (f) is the stroke matching result on the skeleton that obtains according to user's sketch;

Fig. 5 (g) is the stroke skeleton behind the result shown in synthesizing map 5 (d) and Fig. 5 (F);

Fig. 5 (h) is the final stroke decomposition result of Fig. 5 (a);

Fig. 6 is a used stroke feature signal in the one stroke methods of marking;

Fig. 7 is an example of Chinese-character writing form scoring, and five one stroke of this word are shown in Fig. 8 (a)-(e); The scoring of the space structure distribution aesthetics of this word is: (82.5% good, 17.1% general, 0.4% poor), the scoring of its style consistance is: (26.8% good, 51.1% general, 22.1% poor), and its overall aesthetic degree scoring is: (35.2% good, 49.7% generally, 15.1% poor);

Fig. 8 (a) is the 1st stroke of Chinese character shown in Figure 7, and the scoring of its one stroke aesthetics is: (56.0% good, 44.0% general, 0% poor);

Fig. 8 (b) is the 2nd stroke of Chinese character shown in Figure 7; The scoring of its one stroke aesthetics is: (84.7% good, 15.3% general, 0% poor);

Fig. 8 (c) is the 3rd stroke of Chinese character shown in Figure 7; The scoring of its one stroke aesthetics is: (34.8% good, 54.6% general, 10.6% poor);

Fig. 8 (d) is the 4th stroke of Chinese character shown in Figure 7; The scoring of its one stroke aesthetics is: (12.7% good, 46.9% general, 40.4% poor);

Fig. 8 (e) is the 5th stroke of Chinese character shown in Figure 7.The scoring of its one stroke aesthetics is: (9.2% good, 37.5% general, 63.3% poor).

Embodiment

The computer estimation method of Chinese character aesthetics comprises the steps:

1) mode by many people's investigation is the stroke sample scoring that 500～2000 Chinese character input single strokes are drawn, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font one stroke form and its aesthetics evaluation result, uses the learner after the training that font one stroke aesthetics is marked then;

2) mode by many people's investigation is the inner structure scoring of 500～2000 Hanzi specimens, and end user's artificial neural networks, decision tree, the mapping relations between font inner structural features and its aesthetics evaluation result are trained and obtained to fuzzy logic or support vector machine, use the learner after training that the font inner structure is marked then, wherein, the font inner structure comprises the topological relation between each inner stroke of font or radical element, relative position relation, area hides relation, and stroke and stroke, the difference of the spatial relation between stroke and the radical and the standard letter of this word realizes the aesthetics scoring that the each several part space structure in the font is distributed;

3) mode by many people's investigation is the style consistance scoring of 100～300 Hanzi specimens, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font style consistance feature and its aesthetics evaluation result, uses the learner after the training that the font style consistance is marked; Each inner stroke of font or radical element are calculated similarity with the multiple known style font of this word, and determine style consistance scoring of this font with this;

4) according to the one stroke aesthetics scoring that Chinese character is carried out, the scoring of inner structure aesthetics, the scoring of style consistance, the comprehensive every score of applied statistics learning method obtains the overall aesthetic degree scoring of this font.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that described mode by many people's investigation is the stroke sample scoring that 500～2000 Chinese character input single strokes are drawn, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font one stroke form and its aesthetics evaluation result, uses learner after the training that font one stroke aesthetics is carried out methods of marking then and comprise the steps:

A) in advance by many people investigation method, allow six people separately the one stroke image from 100 Chinese characters be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this stroke, that is: add up respectively the number of stroke scoring for " good ", " generally ", " poor ", with it divided by total number of persons, three percentages of gained are respectively the probability of this stroke aesthetics for " good ", " generally ", " poor ", obtain the artificial evaluation result of the aesthetics of 500～2000 one stroke;

B) with the regular script volume image of each stroke described in the step a) standard stroke as this stroke; With these one stroke image parameterizations, promptly extract their profile, track, position signalling, convert the form of vector to, these vectors are carried out pre-service, remove noise, and further extract proper vector, to the proper vector that obtains, make vector subtraction with the proper vector of the standard stroke of this stroke, thereby obtain the morphological differences vector of one stroke;

C) signal difference and the relation of the Function Mapping between the aesthetics evaluation result in the morphological differences vector of one stroke trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; Promptly with morphological differences vector and aesthetics evaluation result respectively as the input and output of artificial neural network, decision tree, fuzzy logic or support vector machine; The artificial evaluation result of applying step in a) implemented the machine learning process that has feedback to artificial neural network or decision tree or fuzzy logic or support vector machine;

D) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that one stroke is marked: promptly to any given one stroke image, applying step b) obtains morphological differences vector between this stroke and its standard stroke, with this vector as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the aesthetics score of this Chinese character stroke.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that the inner structure scoring that described mode by many people's investigation is 500～2000 Hanzi specimens, and end user's artificial neural networks, decision tree, the mapping relations between font inner structural features and its aesthetics evaluation result are trained and obtained to fuzzy logic or support vector machine, use the learner after training that the font inner structure is marked then, wherein, the font inner structure comprises the topological relation between each inner stroke of font or radical element, relative position relation, area hides relation, and stroke and stroke, the difference of the spatial relation between stroke and the radical and the standard letter of this word, realization may further comprise the steps the aesthetics methods of marking that the each several part space structure in the font distributes:

E) in advance by many people investigation method, in advance by many people investigation method, allow six people respectively Chinese character image be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor "; Obtain the artificial evaluation result of 500～2000 font space structure aesthetics by such mode;

F) by the stroke decomposition result with this font parameterization, convert to the vector form; The expression of these vectorial stratification the profile of font, track and relative position information;

G) with the regular script volume image of each font described in the step e) standard letter as this font; Extract in these corresponding vectors of font institute, the topology between each stroke or radical, the signal of geometric relationship compare with the topology of its standard letter, the signal of geometric relationship, obtain its signal difference, promptly do vector subtraction;

H) mapping relations between vector signal difference and aesthetics evaluation result are trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; Applying step e) artificial evaluation result of demarcating in is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine.

I) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that font space structure aesthetics is marked.Promptly to any given font image, applying step f)-step g) obtains the signal difference between the standard letter of this font inner structure and this word, with this difference vector as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the inner structure aesthetics score of this font.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that the style consistance scoring that described mode by many people's investigation is 100～300 Hanzi specimens, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font style consistance feature and its aesthetics evaluation result, uses the learner after the training that the font style consistance is marked; Each inner stroke of font or radical element are calculated similarity with the multiple known style font of this word, and comprise the steps: with this method of determining the style consistance scoring of this font

J) in advance by many people investigation method, allow a plurality of people respectively the style consistance of a plurality of Chinese character images be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: respectively statistics with the number of this word scoring for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this this word mark is " good ", " generally ", " poor "; Obtain 100～300 conforming artificial evaluation results of Chinese character style style by such mode;

K) be ready to write the form sample in regular script body, running hand body and the Li Shu Ti of these Chinese character correspondences, or more other fonts, as known font;

L) pass through the stroke decomposition result with above font parameterization, convert the form of vector to; The expression of these vectorial stratification the profile of font, track and relative position information;

M), calculate the similarity degree of this part various known these parts of font with it to each part of each font; It is one of following two kinds that the computing method of the similarity Sij of one stroke Ai and known font Bj can be chosen wantonly:

(1) with the standard stroke of known font Bj as one stroke Ai, use one stroke aesthetics methods of marking calculates three probable value P1 of this stroke aesthetics, P2, and P3 represents the probability of this stroke aesthetics for " good ", " generally ", " poor " respectively; In the present embodiment, Sij=P1 * 50%+P2 * 50%;

(2) obtain the scope rectangle of stroke Ai, can comprise the minimum rectangle in the rectangle of these all parts of stroke, and by continuous translation with around scope rectangular centre rotation Ai, the writing that makes Ai and the writing of the corresponding stroke of its font Bj overlap the area maximum; If the writing area of Ai is C1, the area of the writing of the corresponding stroke of its font Bj is C2, then Sij=|C1 ∩ C2|/| C1 ∪ C2|, and similarity degree is the ratio of C1, C2 common factor area and C1, C2 union area;

N) the similarity degree polymerization with each part of font and every kind of known font can obtain a matrix, as the style consistance feature of this font;

O) mapping relations between this matrix and aesthetics evaluation result are trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

P) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the font style consistance is marked.Promptly to any given font image, applying step k)-and step n) obtain the similarity between this font and each known font, with this matrix as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the style consistance score of this font.

The computer estimation method of described a kind of Chinese character aesthetics, it is characterized in that the scoring of one stroke aesthetics, the scoring of inner structure aesthetics, the scoring of style consistance that described basis is carried out Chinese character, the comprehensive every score of applied statistics learning method, the method that obtains the overall aesthetic degree scoring of this font comprises the steps:

Q) utilize above-mentioned three methods, use automatic each one stroke aesthetics score, inner structure aesthetics score and the font style consistance score that obtains 100～300 Chinese characters of artificial neural network, decision tree, fuzzy logic or support vector machine after training;

R), allow a plurality of people respectively to step q by many people investigation method) described in Chinese character image do the aesthetics TOP SCORES, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor ";

S) end user's artificial neural networks, decision tree, fuzzy logic or support vector machine are trained and are obtained step q) in score and step r) in the aesthetics TOP SCORES between mapping relations; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

T) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the overall aesthetic degree of font is marked, promptly to any given Chinese character style image, applying step q) method obtains one stroke aesthetics score, inner structure aesthetics score and the font style consistance score of this font, and with these scores as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the overall aesthetic degree score of this font.

As shown in Figure 1, the flow process of embodiment of the present invention system comprises Chinese character image 101, stroke is decomposed and Chinese character parametrization 102, Chinese character aesthetics methods of marking 103, Chinese character aesthetics appraisal result 104; Wherein the composition of Chinese character aesthetics methods of marking 103 comprises Chinese character image sample 201, human scoring 202, machine learning process 203;

Chinese character aesthetics methods of marking 103 in the embodiment of the present invention system comprises study and scoring two parts: the study part, promptly use a machine learning process 203, potential relation between Chinese character image sample 201 and the human scoring 202 is surveyed, and the learner after obtaining training; The scoring part is promptly used the learner that 203 training obtain through the machine learning process, and Chinese character image 101 is calculated its aesthetics score, obtains Chinese character aesthetics appraisal result 104.

Chinese character image 101: Chinese character image is meant the digital picture that comprises Chinese character style; In the present embodiment, all font image all have been separated into individual character one by one, then they are normalized into the two-value black white image (length and width are 300 pixels) of same size; Its example as shown in Figure 3A.

Stroke is decomposed and Chinese character parametrization 102: in the present embodiment, this part may further comprise the steps:

(A) extract its architectural feature from font image, details are as follows for its step (referring to Fig. 3 A, Fig. 3 B, Fig. 3 C):

1) Chinese character image 101 is done refinement (Thinning) and handle, to obtain the skeleton image of this word; Present embodiment has been used the image thinning algorithm that the ACM journal was announced in 1994 (" A noniterativethinning algorithm " ACM Transactions on Mathematical Software, 20 (1): 5-20,1994); Its example is shown in Fig. 3 B;

2) from skeleton image, extract " unique point " (one piece of article " Identification of fork points on the skeletons of handwritten Chinesecharacters " IEEE Transactions onPattern Analysis and Machine Intelligence (PAMI) 21 (10): 1095-1100 that the definition of " unique point " was announced with reference to the IEEE journal in 1999,1999, hereinafter to be referred as document PAMI99), these unique points will be divided into some segment of curve to whole skeleton;

3) all use many end to end straight-line segments to be similar to every segment of curve, concrete steps are as follows: to the segment of curve AB that each bar is not replaced by straight-line segment, establishing A, B is respectively its two ends end points; Calculating is with certain 1 the included angle A CB that C is the summit on the segment of curve AB, and the angle value when angle ACB is maximum then is divided into AC with segment of curve AB, two sections of CB less than a predetermined value (as 135 degree); Otherwise connect AB 2 points with straight-line segment, replace original segment of curve AB; This step constantly carries out all being replaced by straight-line segment until all segment of curve;

4) figure that constitutes by a series of straight-line segments and end points thereof be called as this font " geometric graph " (geometricgraph); " geometric graph " done correction and beta pruning; Present embodiment has been used the skeleton diagram correction technique that uses among the document PAMI99; " geometric graph " example that finally obtains is shown in Fig. 3 C;

(B) calculating the stroke coupling an of the best described in the step (A) between Chinese character style and its standard letter, decompose thereby finish stroke, details are as follows for its step (referring to Fig. 4):

1), obtains " geometric graph " of this standard letter to the standard letter repeating step (A) of the described font of step (A); And the stroke decomposition result of tentative standard font is predicted;

2) between " geometric graph " of " geometric graph " of font described in the step (A) and its standard letter, calculate the stroke matching result an of the best; Present embodiment has been used one piece of article (" Model-based stroke extraction and matching for handwritten Chinesecharacter recognition " .Pattern Recognition that " pattern-recognition " magazine was announced in calendar year 2001,34 (12): 2339-2352,2001) method of heuristic search described in calculates one-to-one relationship between stroke on " geometric graph ";

3) " geometric graph " gone up each stroke track of representing with many straight-line segments, be converted into the stroke decomposition result on former character contour, its concrete grammar is: to the every bit on each straight-line segment on each stroke, with it is that ellipse is drawn in the center of circle, make this ellipse as far as possible big and don't comprise blank parts on any former font image (promptly on the black white image of former font, all pixels in this elliptic region are black), all elliptic region summations of this stroke are the image outline that stroke is decomposed gained;

(C) to differing bigger font with the standard letter form, to finishing the part stroke of coupling in the step (B), use an interactively user interface to assist stroke to decompose, details are as follows for its step (referring to Fig. 5):

1) user comes to describe its framework sketch for font by interactively user interface;

2) revise " geometric graph " that gets by standard letter according to user's sketch; Present embodiment substitutes part stroke counterpart by user's sketch in standard letter " geometric graph " of not finishing coupling in the step (2);

3) repeating step (B) recomputates the optimum matching scheme between stroke, decomposes thereby finish stroke;

(D) to finishing the font that stroke is decomposed, with its parametrization, with the formal representation of vector; Present embodiment has adopted the Chinese character parametric method among the document IS2005, and each font matrix of usefulness all of equal value is represented in vector space.

Chinese character aesthetics methods of marking 103: as previously mentioned, comprise Chinese character image sample 201, human scoring 202, machine learning process 203; Finally the learner that obtains with training comes the result that stroke is decomposed and Chinese character parametrization 102 obtains is done scoring, and obtains Chinese character aesthetics appraisal result 104.

Chinese character image sample 201:, learn for machine learning process 203 but be used in human scoring back with Chinese character image 101;

Human scoring 202: mode by inquiry allows the people of some respectively font image be done scoring (one stroke aesthetics or font inner structure aesthetics or font style consistance), appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor ";

Machine learning process 203: use a learner (as artificial neural network), it is input as the characteristic parameter of this font, and it is output as 3 percentages, represents the probability of this font mark for " good ", " generally ", " poor " respectively; Utilize Chinese character image sample 201 and corresponding mankind's scoring thereof, learner is done training; Use the learner after training that the font aesthetics is made scoring.

As shown in Figure 2, the Chinese character aesthetics is divided into four kinds: one stroke aesthetics scoring 301, inner structure aesthetics scoring 302, inner style consistance scoring 303, overall aesthetic degree scoring 304.

(E) scoring of one stroke aesthetics 301, may further comprise the steps:

1) prepares the Chinese character image sample 201 (being the one stroke image) of some in advance, and it is the mankind marks 202, obtain the human appraisal result of aesthetics of the one stroke of some;

2) to each one stroke, use stroke decompose and Chinese character parametrization 102 in thinning algorithm obtain its skeleton, and use parametric method among the document IS2005 to obtain its parametrization and represent; Get a maximum path on the skeleton; So-called maximum path is meant a discrete curve that is contained in skeleton, and is that to be contained in the discrete curve of skeleton at all be maximum (definition that covers oval Covering Ellipse is referring to document IS2005) for the area sum of the covering ellipse in the center of circle with the point on this curve;

3) to each point on the maximum path, do its normal direction straight line, make the straight line two ends just be positioned at this stroke writing edge and also cross this point, this i.e. the stroke width at this some place;

4) maximum path with this stroke is divided into three sections; Segmentation method comprises following steps: [1] enumerates the position that is divided into three sections two required cut-points; [2] to every kind of segmentation method, the mean value of the stroke width of being had a few on calculating every section remembers that it is w1, w2, w3; [3] as if max{|w1-w2|, | w2-w3|} surpasses a threshold value, then changes step 10), otherwise changes step 5); In the present embodiment, this threshold value value be between the end points of maximum path two ends air line distance 1/4;

5) as shown in Figure 6, to each point on the maximum path, extract it coordinate S=(Sx, Sy), it corresponding cover two one dimension curve M a, the Mi that long axis of ellipse radius and minor axis radius are formed respectively, and skeleton should arrive one dimension curve D of minimum distance composition at font outline edge; These features of being had a few have been formed 5 one dimension curves;

6) note ω=(Sx, Sy, Ma, Mi, D), obtain the standard word of this stroke respective signal ω 0=(Sx0, Sy0, Ma0, Mi0 D0), and obtains difference between ω and the ω 0, establishes ω ^*=ω-ω 0;

7) to ω ^*In every its first order derivative of curve calculation, obtain another collection of curves ω '=(Sx ', Sy ', Ma ', Mi ', D ');

8) to ω ^*In each bar one dimension curve C, obtain the maximal value Cmax on the curve C, mean value Cave, median Cmed; Maximal value Cmax ' to each the bar one dimension curve C among the ω ' on ', obtain curve C ', mean value Cave ', median Cmed ';

9) Cmax '/Cmax, Cave '/Cmax, Cmed '/Cmax, Cmax '/Cave, Cave '/Cave, Cmed '/Cave, Cmax '/Cmed, Cave '/Cmed, Cmed '/Cmed is imported as learner, three values at 0 to 100 real number as the output of learner (respectively to should stroke aesthetics be the probability of " good ", " generally ", " poor "), use this learner to Chinese character image sample 201 used in the step 1) and in conjunction with its artificial appraisal result 202 as learning sample, the relation between F and one stroke appraisal result is learnt; Present embodiment has used reverse transmittance nerve network (Back-Propagating Neural Network) as learner; Change step 11);

10) to each section in three sections of this strokes, be considered as an independent stroke and to its execution in step 5)-8); Cmax '/Cmax with each section in these three sections, Cave '/Cmax, Cmed '/Cmax, Cmax '/Cave, Cave '/Cave, Cmed '/Cave, Cmax '/Cmed, Cave '/Cmed, Cmed '/Cmed imports as learner, three values at 0 to 100 real number as the output of learner (respectively to should the stroke aesthetics being " good ", " generally ", the probability of " poor "), use this learner to Chinese character image sample 201 used in the step 1) and in conjunction with its artificial appraisal result 202 as learning sample, the relation between F and one stroke appraisal result is learnt; Present embodiment has used reverse transmittance nerve network (Back-Propagating NeuralNetwork) as learner;

11) use learner after the study, Chinese character input single stroke is drawn marked;

(F) scoring of inner structure aesthetics 302, may further comprise the steps:

1) prepares the Chinese character image sample 201 of some in advance, and its inner structure aesthetics is the mankind marks 202, obtain the human appraisal result of the inner structure aesthetics of some;

2) extract in these corresponding vectors of font institute topology, several how signal of relation between each several part (as stroke, radicals by which characters are arranged in traditional Chinese dictionaries); The signal that present embodiment uses comprises: to each to stroke x and y, we calculate x and y have a few in the plane mutual ultimate range Lmax, minor increment Lmin and on average these values of distance L ave spatial relationship and topological relation between these strokes can be described out.In addition, our its scope rectangle (bounding box can comprise the minimum rectangle in the rectangle of these all parts of stroke) that on each stroke, draws; Then to per two scope rectangles calculate they in the horizontal direction, lap on vertical direction and the area, be designated as respectively Bh (x, y), Bv (x, y), Bp (x, y);

3) suppose the total n of stroke of this font, by step 2) can obtain 6 n * n matrix, be designated as Mmax, Mmin, Mave, Mh, Mv, Mp, wherein the capable j column element of i of each matrix has been represented the corresponding relation between this font i stroke and j stroke;

4) pairing 6 matrixes of the standard letter of described 6 matrixes of step 3) and this word are subtracted each other, remember that its operation result is Qmax, Qmin, Qave, Qh, Qv, Qp; Together with its inverse matrix totally 12 matrixes, be designated as Qi (i=1 ..., 12);

5), try to achieve the maximal value in its matrix element to each Qi

Minimum value

Maximum value Mean value

Median

Together with first three proper value of matrix λ 1, λ 2, the λ 3 of this Qi, as the input of learner;

6) three values at 0 to 100 real number as the output of learner (respectively to should architectonic beauty sight degree be the probability of " good ", " generally ", " poor "), use this learner to Chinese character image sample 201 used in the step 1) and in conjunction with its artificial appraisal result 202 as learning sample, the relation between inner structural features and its aesthetics appraisal result is learnt; Present embodiment has used reverse transmittance nerve network (Back-PropagatingNeural Network) as learner;

7) learner after the use study is marked to inner architectonic beauty sight degree;

(G) inner style consistance scoring 303 may further comprise the steps:

1) prepares the Chinese character image sample 201 of some in advance, and its inside style consistance is the mankind marks 202, obtain the conforming human appraisal result of inside style of some;

2) be ready to the pairing some kinds of known fonts of font sample, as regular script body, running hand body, Li Shu Ti etc., each font sample all has the known font of m kind, be designated as Bj|j=1,2 ..., m}; To each the subdivision Ai in this font image (as to each one stroke, i=1,2 ..., n), calculate it with every kind of known font in corresponding subdivision similarity degree Sij (i=1,2 ..., n; J=1,2 ..., m), result of calculation is represented with a percentage; Each font image can obtain matrix F={ Sij} of a n * m;

3) with the input of matrix F as learner, three values at 0 to 100 real number as the output of learner (respectively to should font style consistance be the probability of " good ", " generally ", " poor "), use this learner to Chinese character image sample 201 used in the step 1) and in conjunction with its artificial appraisal result 202 as learning sample, the relation between F and the inner style consistance of font appraisal result is learnt; Present embodiment has used reverse transmittance nerve network (Back-Propagating Neural Network) as learner;

4) learner after the use study is marked to inner style consistance;

(H) scoring of overall aesthetic degree 304, comprise following steps:

1) utilizes above-mentioned three methods 301,302,303, use automatic each one stroke aesthetics score, inner structure aesthetics score and the writing style consistance score that obtains 100～300 fonts of artificial neural network, decision tree, fuzzy logic or support vector machine after training;

2) by many people investigation method, allow the people of some respectively above font image be done the aesthetics TOP SCORES, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor ";

3) end user's artificial neural networks, decision tree, fuzzy logic or support vector machine are trained and are obtained score and step 2 in the step 1)) in the aesthetics TOP SCORES between mapping relations; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

4) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the overall aesthetic degree of font is marked.Promptly to any given Chinese character image, the method of applying step (1) obtains one stroke aesthetics score, inner structure aesthetics score and the writing style consistance score of this font, and with these scores as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the overall aesthetic degree score of this font.

Chinese character aesthetics appraisal result 104:, represent the probability of this font aesthetics respectively for " good ", " generally ", " poor " with three percentages with the result of human scoring 202.

Claims (3)

1. the computing machine automatic evaluation method of a Chinese character writing morphology beauty is characterized in that comprising the steps:

1) mode by many people's investigation is the stroke sample scoring that 500～2000 Chinese character input single strokes are drawn, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font one stroke form and its aesthetics evaluation result, uses the learner after the training that font one stroke aesthetics is marked then;

2) mode by many people's investigation is the inner structure scoring of 500～2000 Hanzi specimens, and end user's artificial neural networks, decision tree, the mapping relations between font inner structural features and its aesthetics evaluation result are trained and obtained to fuzzy logic or support vector machine, use the learner after training that the font inner structure is marked then, wherein, the font inner structure comprises the topological relation between each inner stroke of font or radical element, relative position relation, area hides relation, and stroke and stroke, the difference of the spatial relation between stroke and the radical and the standard letter of this word realizes the aesthetics scoring that the each several part space structure in the font is distributed;

3) mode by many people's investigation is the style consistance scoring of 100～300 Hanzi specimens, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font style consistance feature and its aesthetics evaluation result, uses the learner after the training that the font style consistance is marked; Each inner stroke of font or radical element are calculated similarity with the multiple known style font of this word, and determine style consistance scoring of this font with this;

4) according to the one stroke aesthetics scoring that Chinese character is carried out, the scoring of inner structure aesthetics, the scoring of style consistance, the comprehensive every score of applied statistics learning method obtains the overall aesthetic degree scoring of this font;

Described mode by many people's investigation is the stroke sample scoring that 500～2000 Chinese character input single strokes are drawn, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font one stroke form and its aesthetics evaluation result, uses learner after the training that font one stroke aesthetics is carried out methods of marking then and comprise the steps:

A) in advance by many people investigation method, allow six people separately the one stroke image from 100 Chinese characters be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this stroke, that is: add up respectively the number of stroke scoring for " good ", " generally ", " poor ", divided by total number of persons, three percentages of gained are respectively the probability of this stroke aesthetics for " good ", " generally ", " poor ", obtain the artificial evaluation result of the aesthetics of 500～2000 one stroke;

B) with the regular script volume image of each stroke described in the step a) standard stroke as this stroke; With these one stroke image parameterizations, promptly extract their profile, track, position signalling, convert the form of vector to, these vectors are carried out pre-service, remove noise, and further extract proper vector, to the proper vector that obtains, make vector subtraction with the proper vector of the standard stroke of this stroke, thereby obtain the morphological differences vector of one stroke;

C) signal difference and the relation of the Function Mapping between the aesthetics evaluation result in the morphological differences vector of one stroke trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; Promptly with morphological differences vector and aesthetics evaluation result respectively as the input and output of artificial neural network, decision tree, fuzzy logic or support vector machine; The artificial evaluation result of applying step in a) implemented the machine learning process that has feedback to artificial neural network or decision tree or fuzzy logic or support vector machine;

D) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that one stroke is marked: promptly to any given one stroke image, applying step b) obtains morphological differences vector between this stroke and its standard stroke, with this vector as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the aesthetics score of this Chinese character stroke;

The inner structure scoring that described mode by many people's investigation is 500～2000 Hanzi specimens, and end user's artificial neural networks, decision tree, the mapping relations between font inner structural features and its aesthetics evaluation result are trained and obtained to fuzzy logic or support vector machine, use the learner after training that the font inner structure is marked then, wherein, the font inner structure comprises the topological relation between each inner stroke of font or radical element, relative position relation, area hides relation, and stroke and stroke, the difference of the spatial relation between stroke and the radical and the standard letter of this word, realization may further comprise the steps the aesthetics methods of marking that the each several part space structure in the font distributes:

E) in advance by many people investigation method, in advance by many people investigation method, allow six people respectively Chinese character image be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor "; Obtain the artificial evaluation result of 500～2000 font space structure aesthetics by such mode;

F) by the stroke decomposition result with this font parameterization, convert to the vector form; The expression of these vectorial stratification the profile of font, track and relative position information;

G) with the regular script volume image of each font described in the step e) standard letter as this font; Extract in these corresponding vectors of font institute, the topology between each stroke or radical, the signal of geometric relationship compare with the topology of its standard letter, the signal of geometric relationship, obtain its signal difference, promptly do vector subtraction;

H) mapping relations between vector signal difference and aesthetics evaluation result are trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; Applying step e) artificial evaluation result of demarcating in is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine.

I) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that font space structure aesthetics is marked.Promptly to any given font image, applying step f)-step g) obtains the signal difference between the standard letter of this font inner structure and this word, with this difference vector as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the inner structure aesthetics score of this font.

2. the computer estimation method of a kind of Chinese character aesthetics according to claim 1, it is characterized in that the style consistance scoring that described mode by many people's investigation is 100～300 Hanzi specimens, and end user's artificial neural networks, decision tree, fuzzy logic or support vector machine train and obtain the mapping relations between font style consistance feature and its aesthetics evaluation result, uses the learner after the training that the font style consistance is marked; Each inner stroke of font or radical element are calculated similarity with the multiple known style font of this word, and comprise the steps: with this method of determining the style consistance scoring of this font

J) in advance by many people investigation method, allow a plurality of people respectively the style consistance of a plurality of Chinese character images be done scoring, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: respectively statistics with the number of this word scoring for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this this word mark is " good ", " generally ", " poor "; Obtain 100～300 conforming artificial evaluation results of Chinese character style style by such mode;

K) be ready to write the form sample in regular script body, running hand body and the Li Shu Ti of these Chinese character correspondences, or more other fonts, as known font;

L) pass through the stroke decomposition result with above font parameterization, convert the form of vector to; The expression of these vectorial stratification the profile of font, track and relative position information;

M), calculate the similarity degree of this part various known these parts of font with it to each part of each font; It is one of following two kinds that the computing method of the similarity Sij of one stroke Ai and known font Bj can be chosen wantonly:

(1) with the standard stroke of known font Bj as one stroke Ai, use one stroke aesthetics methods of marking calculates three probable value P1 of this stroke aesthetics, P2, and P3 represents the probability of this stroke aesthetics for " good ", " generally ", " poor " respectively; In the present embodiment, Sij=P1 * 50%+P2 * 50%;

(2) obtain the scope rectangle of stroke Ai, can comprise the minimum rectangle in the rectangle of these all parts of stroke, and by continuous translation with around scope rectangular centre rotation Ai, the writing that makes Ai and the writing of the corresponding stroke of its font Bj overlap the area maximum; If the writing area of Ai is C1, the area of the writing of the corresponding stroke of its font Bj is C2, then Sij=|C1 ∩ C2|/| C1 ∪ C2|, and similarity degree is the ratio of C1, C2 common factor area and C1, C2 union area;

N) the similarity degree polymerization with each part of font and every kind of known font can obtain a matrix, as the style consistance feature of this font;

O) mapping relations between this matrix and aesthetics evaluation result are trained and obtained to end user's artificial neural networks, decision tree, fuzzy logic or support vector machine; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

P) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the font style consistance is marked.Promptly to any given font image, applying step k)-and step n) obtain the similarity between this font and each known font, with this matrix as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the style consistance score of this font.

5. the computer estimation method of a kind of Chinese character aesthetics according to claim 1, it is characterized in that the scoring of one stroke aesthetics, the scoring of inner structure aesthetics, the scoring of style consistance that described basis is carried out Chinese character, the comprehensive every score of applied statistics learning method, the method that obtains the overall aesthetic degree scoring of this font comprises the steps:

Q) utilize above-mentioned three methods, use automatic each one stroke aesthetics score, inner structure aesthetics score and the font style consistance score that obtains 100～300 Chinese characters of artificial neural network, decision tree, fuzzy logic or support vector machine after training;

R), allow a plurality of people respectively to step q by many people investigation method) described in Chinese character image do the aesthetics TOP SCORES, appraisal result be three kinds of " good ", " generally ", " poor " one of them; Comprehensive proprietary appraisal result, calculate the mark probability of this font, that is: add up respectively font scoring number for " good ", " generally ", " poor ", respectively with it divided by total number of persons, gained percentage is respectively the probability that this font mark is " good ", " generally ", " poor ";

S) end user's artificial neural networks, decision tree, fuzzy logic or support vector machine are trained and are obtained step q) in score and step r) in the aesthetics TOP SCORES between mapping relations; The evaluation result that using artificial is demarcated is implemented the iterative learning process that has feedback to artificial neural network, decision tree, fuzzy logic or support vector machine;

T) use artificial neural network, decision tree, fuzzy logic or support vector machine after training that the overall aesthetic degree of font is marked, promptly to any given Chinese character style image, applying step q) method obtains one stroke aesthetics score, inner structure aesthetics score and the font style consistance score of this font, and with these scores as the training after artificial neural network or the input of decision tree or fuzzy logic or support vector machine, obtain the output of this moment, be the overall aesthetic degree score of this font.

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