Calligraphy Stroke Extraction in CADAL

2012 ◽  
pp. 368-369
Author(s):  
Kai Yu ◽  
Zhenming Yuan ◽  
Jiangqin Wu
Keyword(s):  
Stroke Extraction

scholarly journals Fast Accurate and Automatic Brushstroke Extraction

2021 ◽  
Vol 17 (2) ◽  
pp. 1-24
Author(s):  
Yunfei Fu ◽  
Hongchuan Yu ◽  
Chih-Kuo Yeh ◽  
Tong-Yee Lee ◽  
Jian J. Zhang
Keyword(s):  
Neural Network ◽  
Efficient Algorithm ◽  
Deep Neural Network ◽  
High Efficiency ◽  
State Of The Art ◽  
High Reliability ◽  
The Other ◽  
Manual Annotation ◽  
Stroke Extraction ◽  
Art Research

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

The automatic generation of Chinese outline font based on stroke extraction

1995 ◽  
Vol 10 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Xiaohu Ma ◽  
Zhigeng Pan ◽  
Fuyan Zhang
Keyword(s):  
Automatic Generation ◽  
Stroke Extraction

AN APPROACH TO NATURAL STROKE EXTRACTION FOR OFF-LINE LOOSELY-CONSTRAINED HANDWRITTEN CHINESE CHARACTERS

2003 ◽  
Vol 17 (08) ◽  
pp. 1483-1513
Author(s):  
DANIEL S. YEUNG ◽  
H. S. FONG ◽  
ERIC C. C. TSANG ◽  
WENHAO SHU ◽  
XIAOLONG WANG
Keyword(s):  
Performance Tuning ◽  
Model Parameters ◽  
Chinese Characters ◽  
Personal Writing ◽  
New Approach ◽  
Stroke Extraction ◽  
Fuzzy Partitioning ◽  
Stroke Models ◽  
Matching Strategy ◽  
Ranked List

This paper proposes a new approach to extracting natural strokes from the skeletons of loosely-constrained, off-line handwritten Chinese characters. It admits the output substrokes from a previously proposed fuzzy substroke extractor as its inputs. By identifying a number of expected ambiguities which include mutual similarities, unstable touches and joint/cross distortions, fuzzy stroke models are constructed and a "hit-all" fuzzy stroke matching strategy is pursued. Fuzzy partitioning technique is used to generate a ranked list of consistent stroke sets from the set of fuzzy strokes being identified. With this approach, a maximum of 20 distinct natural stroke classes can be extracted from each input character, together with an estimate on the actual count of strokes which compose the character. Our system offers a number of performance tuning capabilities such as the computation of the fuzzy scores of each extracted stroke, the adjustment on the fuzzy stroke model parameters, and the potential of incorporating one's personal writing styles into our methodology.

Stroke extraction of handwritten Chinese character based on modified polygonal approximation

1994 ◽  
Author(s):  
Hon-Fai Yau ◽  
Bor-Shenn Jeng ◽  
Ming-Wen Chang ◽  
Chi-Jain Wen ◽  
Char-Shin Miou ◽  
...  
Keyword(s):  
Chinese Character ◽  
Polygonal Approximation ◽  
Stroke Extraction
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