Automatic Evaluation and Generation of
Aesthetic Chinese Calligraphy
I. Calligraphic Characters Representation and Its Acquisition
|
|
|
|
|
The input character |
Its skeletonization result |
Comparing with its standard font |
the stroke extraction result on skeleton |
the stroke extraction result |
An example of stroke extraction from an image of a calligraphic character
|
|
|
|
|
The original character |
Its skeleton trajectory |
With covering ellipses every 50 pixels |
With covering ellipses every 20 pixels |
With covering ellipses every 5 pixels |
An example of calligraphy character representation
II. Evaluating Shapes of Individual Strokes
The strokes signals used for shape grading: w = {Sx ,Sy ,Ma,Mi,D}
|
(a) the evaluation results for individual strokes |
|
(b) the evaluation given by people |
Examples of individual strokes evaluation
(A completely black stroke indicates it is "good" while a completely red one means "bad")
III. Evaluating the Spatial Layout between Strokes
|
|
|
|
|
|
|
100.%-0.00%-0.00% |
61.6%-38.4%-0.00% |
86.8%-13.2%-0.00% |
35.5%-60.0%-4.50% |
31.3%-60.0%-8.70% |
55.0%-45.0%-0.00% |
0.00%-54.3%-45.7% |
100.%-0.00%-0.00% |
70.0%-30.0%-0.00% |
100.%-0.00%-0.00% |
40.0%-60.0%-0.00% |
70.0%-30.0%-0.00% |
90.0%-10.0%-0.00% |
50.0%-50.0%-0.00% |
|
|
|
|
|
|
|
0.00%-54.3%-45.7% |
0.00%-50.7%-49.3% |
98.4%-1.60%-0.00% |
100.%-0.00%-0.00% |
50.3%-49.7%-0.00% |
94.0%-6.00%-0.00% |
57.6%-42.4%-0.00% |
20.0%-60.0%-20.0% |
20.0%-60.0%-20.0% |
100.%-0.00%-0.00% |
60.0%-40.0%-0.00% |
70.0%-30.0%-0.00% |
80.0%-20.0%-0.00% |
70.0%-30.0%-0.00% |
|
|
|
|
|
|
|
90.2%-9.80%-0.00% |
48.1%-51.9%-0.00% |
0.00%-0.00%-100.% |
0.00%-0.00%-100.% |
0.00%-0.00%-100.% |
0.00%-0.00%-100.% |
0.00%-5.90%-94.1% |
80.0%-20.0%-0.00% |
50.0%-50.0%-0.00% |
0.00%-40.0%-60.0% |
0.00%-0.00%-100.% |
0.00%-0.00%-100.% |
0.00%-0.00%-100.% |
0.00%-0.00%-100.% |
(The percents in each unit rate the visual quality of that character, where three values represent its probabilities
to be "good", "so-so" and "bad" respectively from left to right. For comparison, the bottom one comes from humans and
the upper one comes from our algorithm.)
IV. Evaluating Coherence of Stroke Styles
|
|
|
|
|
100.%-0.00%-0.00% |
3.90%-61.2%-34.9% |
91.0%-9.00%-0.00% |
62.7%-37.3%-0.00% |
0.00%-0.00%-100.% |
100.%-0.00%-0.00% |
20.0%-60.0%-20.0% |
70.0%-30.0%-0.00% |
100.%-0.00%-0.00% |
0.00%-20.0%-80.0% |
|
|
|
|
100.%-0.00%-0.00% |
0.00%-9.30%-90.7% |
0.00%-12.0%-88.0% |
58.6%-41.4%-0.00% |
100.%-0.00%-0.00% |
0.00%-40.0%-60.0% |
0.00%-50.0%-50.0% |
40.0%-60.0%-0.00% |
(The percents in each unit rate the visual quality of that character, where three values represent its probabilities
to be "good", "so-so" and "bad" respectively from left to right. For comparison, the bottom one comes from humans and
the upper one comes from our algorithm.)
V. The Overall Evaluation
|
|
|
|
|
|
|
100.%-0.00%-0.00% |
64.5%-35.5%-0.00% |
55.7%-44.3%-0.00% |
67.8%-32.2%-0.00% |
71.1%-28.9%-0.00% |
0.00%-16.4%-83.6% |
100.%-0.00%-0.00% |
100.%-0.00%-0.00% |
60.0%-40.0%-0.00% |
60.0%-40.0%-0.00% |
70.0%-30.0%-0.00% |
80.0%-20.0%-0.00% |
0.00%-0.00%-100.% |
100.%-0.00%-0.00% |
|
|
|
|
|
|
|
43.8%-56.2%-0.00% |
53.7%-46.3%-0.00% |
24.4%-67.3%-8.30% |
14.2%-57.5%-28.3% |
0.00%-57.0%-43.0% |
100.%-0.00%-0.00% |
44.2%-55.8%-0.00% |
20.0%-80.0%-0.00% |
10.0%-90.0%-0.00% |
30.0%-70.0%-0.00% |
20.0%-60.0%-20.0% |
0.00%-10.0%-90.0% |
100.%-0.00%-0.00% |
30.0%-60.0%-10.0% |
(The percents in each unit rate the visual quality of that character, where three values represent its probabilities
to be "good", "so-so" and "bad" respectively from left to right. For comparison, the bottom one comes from humans and
the upper one comes from our algorithm.)
VI. Automatic Calligraphy Generation
(The generated calligraphy fonts are improved with the help of grading module. In each row,
the evaluating grades increase from left to right, corresponding to incremental improvements on the appearance.)
(Automatically generated calligraphy of an ancient poetic verse from the Tang Dynasty in the 8th century. The five
learning sources in this experiment are shown in (u-1)每(u-5). Ten automatic calligraphy generation results using the calligraphy
visual quality grading component as the feedback are shown in (u-6)每(u-15). For comparison, we show five automatic calligraphy
generation results without employing the grading component as the feedback in (u-16)每(u-20). As judged by many Chinese
scholars participating in the experiment, the visual quality of the calligraphic writings in (u-6)每(u-15) is clearly and significantly better
than that of (u-16)每(u-20).)