Search Results (13)

This article examines Xinzeng Gegu yaolun 新增格古要論 (Expanded Essential Criteria of Antiquities), a connoisseurship manual compiled in 1460 by the mid-Ming official Wang Zuo 王佐. Drawing upon Cao Zhao’s 曹昭 early Ming Gegu yaolun 格古要論 (Essential Criteria of Antiquities), Wang reconfigured a manual focused on authentication and appreciation into a text structured by Confucian values and political ethics. He added ritual-oriented entries in chapters four and ten through thirteen, such as “An Examination of Song Dynasty Attire and Rank Titles” 宋制服裝入銜考 and “An Inquiry into Gold and Silver Insignia” 佩金銀牌考, reinforcing Confucian ideology through commentary on ritual institutions and the inclusion of imperial edicts and commemorative inscriptions. He also reorganized the placement of guqin 古琴, calligraphy and painting, while redefining evaluative standards to integrate material objects into moral instruction and bureaucratic discipline. In doing so, Wang reinforced a shared community of scholar–officials, using ritual hierarchy, loyalist writings, and gift exchange to respond to the uncertainty of a fractured political order. This article argues that through a non-canonical text like Xinzeng Gegu yaolun, mid-Ming scholar–officials extended Confucian discourse into antiquities, transforming antiquarian writing into a visible enactment of ethical values and collective identity. Full article

Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers’ styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision. Full article

The essence of Chinese calligraphy inheritance resides in calligraphy education. However, it encounters challenges such as a scarcity of calligraphy instructors, time-consuming and inefficient manual assessment methods, and inconsistent evaluation criteria. In response to these challenges, this paper introduces a deep learning-based automatic calligraphy evaluation model. Initially, hard-pen handwriting samples from 100 volunteers were collected and preprocessed to create a dataset consisting of 4800 samples, along with the corresponding label files for hard-pen calligraphy evaluation. Subsequently, YOLOv5 was utilized for region detection and character recognition on the evaluation samples to obtain the corresponding standard samples. Lastly, a Siamese metric model, with VGG16 as the primary feature extraction submodule, was developed for hard-pen calligraphy evaluation. To improve feature extraction and propagation, a transformer structure was introduced to extract global information from both the evaluated and standard samples, thereby optimizing the evaluation results. Experimental results demonstrate that the proposed model achieves a precision of 0.75, recall of 0.833, and mAP of 0.990 on the hard-pen calligraphy evaluation dataset, effectively realizing automatic calligraphy evaluation. This model presents a novel approach for intelligently assessing hard-pen calligraphy. Full article

Kozo paper, usu-mino-gami, is frequently used as the first back lining paper of hanging scrolls in order to support the main paper with a painting or a work of calligraphy on it. To dye it an appropriate color, paper is often treated with an alkali mordant solution. However, current kozo paper products have received such comments from conservators that wet tensile strength is weak and hard to handle. Therefore, improving the wet tensile strength of kozo paper is required. In previous papers, the effect of the sheet forming method, cooking condition, and parenchyma cell content between fibers on the wet tensile strength of kozo paper has been investigated. In this paper, the effect of glucuronoxylan, the main component of hardwood hemicellulose on the wet tensile strength of kozo paper was investigated. The wet tensile strength of kozo paper, when made in different cooking conditions, was evaluated using the Finch device. Glucuronoxylan content in fiber was analyzed using GC-FID. According to the results, it has been proved that glucuronoxylan content (with a xylan to glucan molar ratio of 4.43% to 5.16%) itself contributes to the wet tensile strength of the kozo sheet. Therefore, to increase the wet tensile strength of kozo paper, it is recommended to cook under milder conditions, thus retaining a higher amount of glucuronoxylan in the pulp. Full article

In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate. Full article

The Chinese writing system, known as hanzi or Han character, is fundamentally pictographic, composed of clusters of strokes. Nowadays, there are over 85,000 individual characters, making it difficult even for a native speaker to recognize the precise meaning of everything one reads. However, specific clusters of strokes known as indexing radicals provide the semantic information of the whole character or even of an entire family of characters, are golden features in entry indexing in dictionaries and are essential in learning the Chinese language as a first or second idiom. Therefore, this work aims to identify the indexing radical of a hanzi from a picture through a convolutional neural network model with two layers and 15 classes. The model was validated for three calligraphy styles and presented an average F-score of ∼95.7% to classify 15 radicals within the known styles. For unknown fonts, the F-score varied according to the overall calligraphy size, thickness, and stroke nature and reached ∼83.0% for the best scenario. Subsequently, the model was evaluated on five ancient Chinese poems with a random set of hanzi, resulting in average F-scores of ∼86.0% and ∼61.4% disregarding and regarding the unknown indexing radicals, respectively. Full article

Background: Dementia is a common disease in aging populations. The treatment has mainly focused on memory decline prevention and behavior control. Nonpharmacological treatments, such as cognition training, physical exercise, and music therapy have been effective in slowing memory decline. Chinese calligraphy handwriting (CCH) through breath regulation and fine hand control involves high concentration levels, emotion regulation, and self-awareness. CCH is a mind and body activity that is culturally relevant to older Chinese adults. This study evaluated the beneficial effects of CCH on mild cognitive impairment. Methods: In 2018, we conducted 8 weeks of CCH training at the Tri-Service General Hospital. The participants were asked to copy a regular script. At the end of the course, they gave oral presentations and showed their work. Self-report questionnaires on emotion, memory, upper limb coordination, attention, and language were collected before and after training. Results: The five questionnaires showed significantly positive feelings after CCH training. The conditions of emotional stability, concentration, hand movement, memory, and speech improved. Conclusions: CCH training stimulated the brain and improved cognition, psychological symptoms, and hand stability. It is inexpensive and worthwhile for elderly Chinese individuals with mild cognitive impairment to take time daily to practice calligraphy. Full article

“Qi” plays an important role in Chinese Calligraphy Theory. “Qi” deeply embodies the spirit of Taoism and philosophical speculation with Oriental characteristics and can reflect the core and composition of traditional Chinese art aesthetics. The “qi” of calligraphy determines the character of calligraphy works and is the soul of calligraphy. When “qi” is weak, the quality of calligraphy works is poor, but calligraphy works will have a high style when they are full of “qi”. “qi” governs the aesthetic evaluation and artistic creation of Chinese calligraphy, which runs through the whole history of Chinese calligraphy. From the point of view of Chinese philosophy of information, “qi” is a kind of comprehensive information conveyed in calligraphy works. Calligraphy works should capture important information in such aspects as handwriting potential, romantic charm, strength, significance and antiquity, so as to form the unique character of calligraphers. Full article

This study aimed to investigate the effects of finger grip and wrist position on Chinese calligraphy handwriting (CCH). Thirty participants were recruited in the study and asked to manipulate the Chinese brush using two finger grip methods (three-finger grip and five-finger grip) and two wrist positions (suspended wrist and raised wrist). Three experimental writing tasks were applied to investigate writing stability, agility, and hand–eye coordination, and to evaluate the completion time (s), area of error (cm²), and error times. Subjective responses (arm aching level, ease of grip, exertion level, and comfort) regarding the four combinations of Chinese brush manipulation were measured. The results indicated significantly better performance with the three-finger grip for the stability and agility tests, and with the five-finger grip for the hand–eye coordination task. Using the suspended wrist position for CCH allowed better agility and hand–eye coordination than the raised wrist position. In consideration of the results of the four operational combinations, the three-finger grip with a suspended wrist position demonstrated the best performance in both objective and subjective measurements. It is recommended for application in the early learning stage. These findings can be considered when teaching Chinese brushes for beginners of CCH in schools. Full article

This study proposed a calligraphy brush trajectory model for the behavior of brush movements and provided the three-dimensional handle coordinates for a robotic arm to write calligraphy. This study dealt with basic footprints and bent lines of calligraphy and proceeded as follows. The shape of brush footprints on paper was measured, which provided the positions of the brush relative to its handle. These brush footprints were scanned and corrected for skew using the Direct Linear Transformation. The outer frame of each basic footprint was characterized using Bézier curves. Bent lines were drawn to derive the brush trajectory model, and it was used to derive the relationship between the trajectories of the brush and handle. By characterizing the changes in the footprints with handle displacement, we obtained the relationship between the handle coordinates and the position and shape of the brush footprints. The written characters were evaluated based on their size, position, and stroke balance, with a maximum score of 100 in each category. The average score of the “Yong” character written using our brush trajectory model was approximately 94 points; when the handle coordinates were fixed to the center of each footprint, the average score was only 88 points. Full article

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing. Full article

The most popular way of learning oriental calligraphy has been by practicing the calligraphy under the supervision of a human teacher, but finding a good instructor can be difficult. There are a number of studies in the literature that have evaluated calligraphic characters in holistic ways, but such systems do not support detailed supervision of scripting errors. This study proposes a Kanji calligraphy learning system with computerized supervision and analyzes the learning efficiency of the system, where the supervision includes symmetries between strokes. The proposed system compares a written calligraphic character of a user to the model of a human expert, and indicates error spots with explanations. An experiment with 22 participants proved that this system was more efficient at reducing the number of scripting errors in comparison to the traditional manner of a human expert. The main contribution of this paper was to identify and reveal the efficacy of computerized supervision in comparison to a human supervisor. The proposed system decreased the writing-error-rates of learners from 32.7% to 3.4%, whereas the traditional practice reduced the error rates from 31.0% to 6.8%. This result shows that computerized supervision is more efficient than human supervision for learning calligraphy. Full article

Sensors can monitor physical attributes and record multimodal data in order to provide feedback. The application calligraphy trainer, exploits these affordances in the context of handwriting learning. It records the expert’s handwriting performance to compute an expert model. The application then uses the expert model to provide guidance and feedback to the learners. However, new learners can be overwhelmed by the feedback as handwriting learning is a tedious task. This paper presents the pilot study done with the calligraphy trainer to evaluate the mental effort induced by various types of feedback provided by the application. Ten participants, five in the control group and five in the treatment group, who were Ph.D. students in the technology-enhanced learning domain, took part in the study. The participants used the application to learn three characters from the Devanagari script. The results show higher mental effort in the treatment group when all types of feedback are provided simultaneously. The mental efforts for individual feedback were similar to the control group. In conclusion, the feedback provided by the calligraphy trainer does not impose high mental effort and, therefore, the design considerations of the calligraphy trainer can be insightful for multimodal feedback designers. Full article