Calligraphy Characters Research Articles

A Study on the Visual Perception of Cultural Value Characteristics of Traditional Southern Fujian Architecture Based on Eye Tracking

The traditional Southern Fujian architecture serves as a reflection of the region’s deeply rooted cultural values. However, research on the architectural and cultural heritage of Southern Fujian remains scarce. Accordingly, this study employs eye-tracking technology to experiment with 20 individuals with local cultural backgrounds and 20 without it to investigate the differences in the perception of the architectural heritage of Southern Fujian by individuals with disparate cultural backgrounds. The findings indicate that distinct groups evince disparate visual perceptions of the architectural and cultural heritage of Southern Fujian. The participants’ attention was divided into two categories: patterns and shapes and calligraphy and text. Non-locals were primarily drawn to patterns and shapes, whereas locals demonstrated a greater interest in the calligraphy and text category. The extraction of cultural information from calligraphic characters requires greater understanding and more time spent within the culture. Knowledge of local cultural backgrounds is helpful to better understand such information. This highlights the potential impact of Southern Fujian architecture on the local population. These findings provide a compelling rationale for the incorporation of multicultural elements in contemporary architectural design while also advocating for the preservation and advancement of Southern Fujian architectural heritage.

4D Analysis and Research of Art Painting Intelligent Fusion Based on Calligraphy Character Image Edge Recognition Algorithm

4D Analysis and Research of Art Painting Intelligent Fusion Based on Calligraphy Character Image Edge Recognition Algorithm

An End-to-End Generation Model for Chinese Calligraphy Characters Based on Dense Blocks and Capsule Network

An End-to-End Generation Model for Chinese Calligraphy Characters Based on Dense Blocks and Capsule Network

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

AbstractThe Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.

A study of feature extraction for Arabic calligraphy characters recognition

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.

Correction: LanT: finding experts for digital calligraphy character restoration

Correction: LanT: finding experts for digital calligraphy character restoration

LanT: finding experts for digital calligraphy character restoration

LanT: finding experts for digital calligraphy character restoration

A Study of the Intersection of Traditional Calligraphy and Digital Media Art

Abstract As a classic representative of traditional culture, Chinese calligraphy art has more and more obvious advantages in today’s digital media art creation. In this paper, the calligraphic text image is binarized by the OTSU method, the image enhancement is realized by linear gray value stretching, and the optimal tilt correction method is used to preprocess the traditional calligraphic text image and make a font library. Based on the data from the calligraphy font library, an immersive digital interactive context of traditional calligraphy is designed using digital media art. Pix2Pix network is introduced to combine with the PatchGAN network to establish a style migration network for traditional calligraphy text images, and three different types of loss functions are designed to ensure the accurate generation of traditional calligraphy text. The L1 loss of this paper’s model in generating traditional calligraphy characters is only 0.0359 at the lowest, the similarity values of the generated samples of calligraphy characters are mainly between 90% and 96%, and the maximum error of the relative position and relative size of the radicals is only 1.18%. The average value of the G2R indicator for the users’ images of calligraphic characters was 75.23%, and 89.86% of the users indicated that they were basically satisfied with the immersive interactive context of traditional calligraphy. Traditional calligraphy can achieve digital transformation and provide audiences with more diverse cultural experiences by relying on digital media art.

Research on Algorithm for Authenticating the Authenticity of Calligraphy Works Based on Improved EfficientNet Network

Calligraphy works have high artistic value, but there is the rampant problem of forgery. Indeed, the authentication of traditional calligraphy heavily relies on calligraphers’ subjective judgment. Therefore, spurred by the recent development of neural networks, this paper proposes a method for authenticating calligraphy works based on an improved EfficientNet network. Specifically, the developed method utilizes the character box algorithm to efficiently extract individual calligraphy characters, which are then augmented and used as the training set for the model. The training process employs CBAM and Self-Attention modules to enhance the attention mechanism of the EfficientNet network. The trained network model is used to judge the calligraphy works’ similarity; tested on authentic works, imitated works, and works from other calligraphers; and compared with other networks. The experimental results demonstrate that the proposed method effectively achieves the authentication of calligraphy works, and the improved CBAM-EfficientNet network and SA-EfficientNet network achieve better authentication performance.

전서와 행초서의 혼융체(渾融體)로 표현되는 새로운 서예 장르

The four main styles of Chinese calligraphy characters are in the general order of their appearance: seal script, clerical script, regular script, and cursive­semi­cursive script. Generally, all the characters in a calligraphic art work have a consistent type of script. In this paper, we develop a new genre of calligraphy art, in which the components constituting one character are expressed into the seal script and cursive­semi­cursive script, which is a fusion of the two scripts.
 The fusion of these scripts brings forth various aesthetic characteristics by greatly expanding the formativeness of the scripts. This fusion script maintains the aesthetic characteristics of each of the two scripts, and in combined form, it shows its own diverse yet unique aesthetic aspects.
 The calligraphic art work in this fusion script genre inherits the artistry in both of the seal script genre and the cursive­or­semi­cursive script genre. Furthermore, as a synergy effect of the two genres, it has infinite potential artistry and displays an inspiringly profound and rich yet unique artistic atmosphere and inspiration.

Glancing back at your hearing: Generating emotional calligraphy typography from musical rhythm

Typography design influences users’ reading experiences and emotional responses. Within the realm of Human–Computer Interaction (HCI), numerous researchers have focused on typography design by exploring the generation of parameterized typefaces, encompassing aspects such as character size, line spacing, and word spacing. However, scant attention has been given to non-parameterized typefaces, particularly calligraphy characters. Calligraphy, a profoundly expressive and quintessential art form in China, serves as a reflection of human emotions. Its association with music has garnered considerable attention within the field of HCI. In this paper, we introduce Inkphony, a system that leverages musical rhythm to generate calligraphy typography and its composition based on the conveyed emotion. Through empirical evaluations, we demonstrate the efficacy of our approach in accurately expressing varying levels of emotion, while also establishing a perceptible connection between typography and music. Our work aims to provide an intelligent cross-modal tool for music visualization and artistic creation, thereby contributing to the field of HCI.

Easy recognition of artistic Chinese calligraphic characters

Easy recognition of artistic Chinese calligraphic characters

Dynamic Reconstruction Algorithm of Calligraphy Characters Based on Self-organizing Mapping

Dynamic Reconstruction Algorithm of Calligraphy Characters Based on Self-organizing Mapping

Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

AbstractRobot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of ‘stroke‐radical‐part‐character’. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

Visual Attention Adversarial Networks for Chinese Font Translation

Currently, many Chinese font translation models adopt the method of dividing character components to improve the quality of generated font images. However, character components require a large amount of manual annotation to decompose characters and determine the composition of each character as input for training. In this paper, we establish a Chinese font translation model based on generative adversarial network without decomposition. First, we improve the method of image enhancement for Chinese character images. It helps the model learning structure information of Chinese character strokes to generate font images with complete and accurate strokes. Second, we propose a visual attention adversarial network. By using visual attention block, the network catches global and local features for constructing details of characters. Experiments demonstrate our method generates high-quality Chinese character images with great style diversity including calligraphy characters.

Translating Chinese Calligraphy Into Indonesian: A Philological Approach

This paper is entitled ”Translating Chinese calligraphy into Indonesian: A Philological Approach.” Furthermore, the researcher intends to: (1) present a translation of Chinese calligraphy script in Indonesian, (2) express the moral message contained in Chinese calligraphy script. This study used a philological approach, which consisted of manuscript research methods and text editing, utilizing Lucien Goldman’s theory of philological explanation. Based on the Chinese calligraphy script found in Tjong A Fie’s mansion, the researchers made pragmatic structural observations and presented them from descriptive, analytical and comparative stages. The results of this study are the reconstruction of calligraphy characters as the content of the text that is abstract and essential; to be precise the Chinese calligraphy characters are found at the front gate, main entrance, front door and back right-wing of Tjong A Fie’s mansion. In this pragmatic observation of the Chinese calligraphy script, Chinese social and cultural values are obtained, such as the belief system, philosophy, and the relationship between humans and nature. These values are applied by the Chinese community as a guideline hence they can live in peace, harmony and balance.
 Keywords: Chinese Calligraphy; philological approach; Chinese community

Intelligent Evaluation Method of Calligraphy Characters Based on Deep Stroke Extraction

Intelligent Evaluation Method of Calligraphy Characters Based on Deep Stroke Extraction

A Chinese Calligraphy-Writing Robotic System Based on Image-to-Action Translations and a Hypothesis Generation Net

This paper attempts to use a delta robot’s structure and reliable coordinates to develop a self-learning Chinese calligraphy-writing system that requires precise control. Ideally, to achieve human-like behavior, a delta robot can learn stroke trajectories autonomously and present the stroke beauty of calligraphy characters. Unfortunately, state-of-the-art approaches have not yet considered the presentation of stroke beauty resulting from angles of rotation and tilt of the brush. This paper presents an integrated system consisting of a stroke processing module, a hypothesis generation net (HGN) learning model, a delta robot, and an image capture module. Our approach utilizes both the stroke trajectories from the stroke processing module and angles information from the HGN learning model to automatically produce five degrees of freedom action instructions. Based on the instructions, the delta robot completes calligraphy writing. Then, the image capture module provides feedback to the writing system for error calculation and coordinate correction. We utilize the mean absolute percentage error to verify the performance of the writing results. A correction algorithm and linear regression were used to improve the error correction results (less than 2% error). After several cycles, the written results approached the target sample finally. Consequently, the written results produced by the delta robot prove that our proposed system is capable of self-learning and correction.

集安 출토 고구려 銘文塼 고찰

We looked into bricks engraved with letters that were excavated from Cheonchuchong(千秋塚) and Taewangleung(太王陵) during the Koguryo period.
 It is thought that four scribes were involved in the production of the bricks engraved with letters excavated from Cheonchuchong.
 The first type is Aa type, Ab type, Ad type, the second type is Ac type, Ae type, Af type, the third type is Ag type, and the fourth type is Ba type and Bb type. These scribes share similar typefaces with each other and use their unique brushstrokes to show Koguryo's calligraphy character culture.
 The bricks engraved with letters excavated from Taewangleung can be divided into two types, but they are likely to be the same person because of the similar font. In addition, there is a possibility that the writing style was related or influenced because the typeface was similar to the inscribed bricks excavated from Cheonchuchong.
 The brick letters excavated from Cheonchuchong and Taewangleung stand out for their character design sense. This shows that the writing culture of Koguryo was recognized as writing art that utilized aesthetic sense rather than simply writing.