Calligraphic Images Research Articles

Arabic Calligraphy Image Analysis With Transfer Learning

Arabic Calligraphy Image Analysis With Transfer Learning

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.

Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation

Abstract In order to study the image recognition technology of traditional Chinese painting and calligraphy based on pseudo linear directional diffusion equation. Firstly, in the pixel domain, an overall style feature extraction algorithm of ink painting based on histogram is proposed, and then the local area with the most representative stroke style is located based on Sobel edge detection method to obtain the local detail style information describing the stroke. Then, using image processing, computer vision and other technologies, combined with some quantifiable authenticity identification indexes of Chinese painting, this paper designs a authenticity identification method of Chinese painting based on image recognition to assist the authenticity identification of Chinese painting and improve the reliability of authenticity identification. The experimental results show that the fusion algorithms based on information entropy and distance are proposed in this paper. On the premise of maximizing the artistic style, the fusion algorithm combines the advantages of the overall and local class results, so as to realize the automatic classification of ink painting based on artistic style and output the classification results.

Application of Deep Neural Networks and Human-Computer Interaction Technology in Art and Design Area

Technological development has given a new dimension to the art and design area. This research implements the deep learning model for human-computer integration technology in art and design with wireless sensor networks. In this human-computer interaction, each person and the devices are treated as network nodes. The user will act as a node requesting access privileges for utilizing the calligraphy design to learn the strokes in the self-pace mode for studies. In this research, the human-computer interaction is implemented for recognizing the strokes and curves in the calligraphy images loaded by the user. This identification will aid the user in learning different styles of calligraphy. The proposed fruit-fly optimization algorithm (FFOA) method is analyzed for high classification accuracy, less delay time, and reduced noise parameters against specific existing algorithms.

A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image

A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image

The impact of action observation and motor imagery on the aesthetic preference of Chinese calligraphy.

The creation of artwork requires motor activity. However, few empirical studies have directly explored the relationship between the experience of an artist's action and aesthetic experience. This study aimed to examine the effect of observing and imagery of the artist's action on the participants' aesthetic preferences. In Experiment 1 and 2, we took hard-pen and brush-pen Chinese calligraphy images as the stimulus, respectively, to explore the influence of action observation on the aesthetic preference by manipulating the artists' actions. The results of both Experiment 1 and 2 show that when participants observed the artists' actions, they tended to report a higher preference for calligraphy images compared with the control condition. In Experiments 3 and 4, we used instructions to manipulate the motor imagery tasks and investigated the effect of imaging the artist's action on the participants' aesthetic preferences. The results showed that both kinesthetic imagery and visual imagery increased the participants' preference. In general, our study shows that both action observation and motor imagery contribute to participants' aesthetic preferences. The results are discussed in terms of how artists' actions possibly influence the aesthetic preference of Chinese calligraphy.

The effect of action observation on aesthetic preference of Chinese calligraphy: An fMRI study.

IntroductionThere is some evidence suggesting that movement perception has an effect on aesthetic experience. However, the neural mechanisms underlying the observation of creators’ creative action (the process that calligraphers create calligraphy) remain unclear.MethodsIn this study, participants were scanned with fMRI while performing aesthetic judgments on Chinese calligraphy images with/without action observation.ResultsBehavioral results showed that both the work by the expert and novice with action observation were rated significantly higher on aesthetic preference than those without action observation. Imaging results showed that brain regions associated with perceptual, cognitive, and emotional processing were commonly activated by calligraphy images with/without action observation. However, compared with no action observation, aesthetic judgments of calligraphy images with action observation elicited stronger activation in the anterior cingulate cortex and the bilateral insula. Meanwhile, the superior parietal lobe which is associated with relevant inner action imitation, was also activated when observing the creator's action.ConclusionsBrain activation in the superior parietal lobe, anterior cingulate cortex, and the bilateral insula indicated that observing the creative action of the creators contributed to the aesthetic experience of the observer.

SSGAN: generative adversarial networks for the stroke segmentation of calligraphic characters

At present, the Chinese government is encouraging people to learn calligraphy; however, an automatic evaluation method for the results is not available. Calligraphy evaluation is challenging, because calligraphic characters are complex graphics composed of strokes as basic units. Therefore, it is necessary to consider not only the structure of the whole character but also the details of each stroke. To address this problem, we propose an automatic stroke segmentation method for calligraphic images as the foundation for the subsequent evaluation task. Specifically, we treat the stroke segmentation problem as an image to image translation problem and propose the stroke segmentation generative adversarial network (SSGAN) algorithm. Unlike the existing approaches that do not exhibit a satisfactory performance or are not suitable for use in practical projects, the proposed approach can efficiently obtain accurate results. The SSGAN enables the simultaneous generation of all the strokes of a calligraphic image, based on a multistroke tensor training strategy. Moreover, we specifically design a ResU-Net structure with embedded attention modules to enhance the accuracy of the results. The experimental results demonstrate the superiority of the proposed method over the existing state of the art models.

Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

This paper presents a new computational backend model that supports Arabic document information retrieval (ADIR) as a dataset and OCR services. Therefore, different services that support document analysis, retrieving, processing including dataset preparation, and recognition will be discussed. Consequently, ADIR services provide general functions of the Arabic OCR to compose many other services in the OCR domain. Furthermore, the proposed work can provide accessing different methods of document layout analysis with a platform where they can share and handle such methods (services) without any setup requirements. One of the used datasets composed from 16,800 Arabic letters written by 60 writers. Each writer wrote each letter from Alif to Ya 10 times in two forms. The forms were scanned at 300 DPI resolution and are segmented in two sets: training set with 13,440 letters for 48 images per class label, and testing set with 3,360 letters to 120 images per class label Convolutional neural network (CNN) is used and adapted for Arabic handwritten letters classification. In an experimental test, we showed that our results outperform 100% classification accuracy rate on testing images. Therefore, the ADIR services provide a “service description”, which includes an interface and a server's URL. The interface allows communication process between clients and services. Although, in this article we evaluate IR results and compared them with respect to corrected equivalent.

An end-to-end model for chinese calligraphy generation

A Chinese calligraphy copybook usually has a limited number of Chinese characters, far from a whole set of characters needed for typesetting. Therefore, there is a need to develop complete sets of Chinese calligraphy libraries for well-known calligrapher styles. This paper proposes an end-to-end network for character generation based on specific calligraphy styles. Specifically, a style transfer network is designed to transfer the style of characters, and a content supplement network is designed to capture the details of stylish strokes. Our model can generate high-quality calligraphy images without manually annotating data. To verify the generated calligraphy styles, a new dataset is constructed for experimental comparison between our method and two other baseline methods. Moreover, a user study is conducted to evaluate our generated calligraphy from a visual perspective. When the experiment participants are asked to distinguish the real calligraphy from generated samples, the correct rate was 53.5%. The results show that the calligraphy styles generated by our model are almost indistinguishable from the original works.

Research on personalized recommendation hybrid algorithm for interactive experience equipment

AbstractInteractive calligraphy experience equipment has the characteristics of a large amount of data, various types, and strong homogeneity, which makes it difficult for users to find interesting resources. In this article, a hybrid personalized recommendation algorithm is proposed, which uses collaborative filtering and content‐based recommendation methods in turn to make recommendations. In the initial recommendation, Latent Dirichlet Allocation (LDA) topic model is used to reduce the dimension of high‐dimensional user behavior data and establish a user‐writing theme matrix to reduce inaccurate recommendation caused by high sparsity data in collaborative filtering algorithm. The user interest list is obtained by calculating the similarity between users. Then, on the basis of the preliminary recommendation results, VGG16 model is used to extract the feature vector of the calligraphy image and calculate the similarity between the user's calligraphy words and the primary recommended calligraphy words, thus obtaining the final recommendation results. The experimental results verify the effectiveness and accuracy of the recommendation algorithm, which are better than other recommendation algorithms on the whole, and have important engineering guiding significance.

The Calligraphy Image Generation of Great Calligrapher, Wan Xizhi by Zhao Mengfu

The Calligraphy Image Generation of Great Calligrapher, Wan Xizhi by Zhao Mengfu

A novel generative adversarial net for calligraphic tablet images denoising

Chinese calligraphic images have important historical and artistic value, but natural weathering and man-made decay severely damage these works, thus image denoising is an important topic to be addressed. Traditional denoising methods still leave room for improvement. In this paper, image denoising is modeled as generation of clean image by using GAN (Goodfellow I et al. Advances in Neural Information Processing Systems 2672–2680, 2014) with an embedment of residual dense blocks (Zhang Y et al. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018) that was formerly used for super resolution reconstruction. Meanwhile, a new type of noise is defined to simulate the real noise, and is used for compensation of unpaired data in the training set for GAN. The new structure, used with some preprocessing and training methods, yield satisfactory results compared to known denoising methods.

Writing process restoration of Chinese calligraphy images based on skeleton extraction

In this paper, a writing process restoration method for static Chinese calligraphy works is proposed. Through this method, the initial skeleton of the Chinese character calligraphy image is extracted first. Then, the distortion elimination and sorting processes are performed for the initial skeleton, and the accurate skeleton of the Chinese character image is obtained. Finally, the appropriate ink dots are selected to move and render along the skeleton, thus obtaining the dynamic writing effects. Regarding the aspect of skeleton distortion elimination, the initial skeletons are first classified into three different types; then, the processing algorithms are designed respectively according to their morphological features, thus realizing highly efficient recognition and elimination for the skeleton distortion. This method can realize rapid dynamic restoration for calligraphy fonts, such as regular script.

A survey of digital calligraphy

Unlike its traditional counterpart, digital Chinese calligraphy is created and presented using digital technology in human-computer interaction environments. Here, we provide a state of the art introduction to digital calligraphy research. After analyzing the research background and goal of digital calligraphy, we present the most important topics of digital calligraphy: calligraphic tool modeling, calligraphic image analyzing and processing, and calligraphic shape analysis and synthesis. Each research topic is accompanied by a state-of-the-art introduction and current trends of the topic. Finally, we discuss important issues concerning the further development of digital calligraphy.

Zen and the Calligraphic Image: Postwar Australian Abstraction, 1950s–1960s

Zen and the Calligraphic Image: Postwar Australian Abstraction, 1950s–1960s

Artistic information extraction from Chinese calligraphy works via Shear-Guided filter

Chinese calligraphy is a unique visual art, and is still emitting fascinating artistic brilliance and charm. The artistic beauty of Chinese calligraphy is constituted by two elements, i.e., form and spirit, which attract continual attentions by calligraphy artists. In this paper, we present Shear-Guided filter to extract the artistic information. Shear-Guided filter is designed by combining the shear transformation and the guided filter. It has both the edge-preserving smoothing property from the guided filter and the linear multidirectional property from the shear transformation. Shear-Guided filter can extract more spirit information from the calligraphy images, especially in the half-dry strokes. Furthermore, the previous evaluation parameters consider all pixels equally, which is against the truth. Here we put forward three new parameters to solve this issue. Finally, experiments show that Shear-Guided filter can extract the information of form and spirit more accurately, and the new evaluation parameters have better performances in evaluation.

Comparison of different image denoising algorithms for Chinese calligraphy images

Rubbing is one of the most universal and perhaps the oldest of the techniques that have been used in printmaking. A carefully made rubbing provides an accurate and full-scale facsimile of the surface reproduced. However, many rubbing have been destroyed or lacked a good ways to identify them by certain events, while some other contained a large white background, or have become illegible due to erosion. In order to correct interpretation of these images, some image restoration techniques are employed. Image denoising is one of the important fields in the restoration arena. But, a great challenge of image denoising is how to preserve the edges and all fine details of a rubbing image while reducing the noise. This paper presents a comprehensive comparative study of image denoising techniques relying on Anisotropic Diffusion filter, Wiener filter, TV (Total Variation), NLM (Non-Local Means, NLM), Bilateral filtering. A quantitative measure of comparison is provided by the PSNR, MSE, SNR, UQI and SSIM of the image. Finally, the paper also analyzes its effect of denoising on rubbings with various algorithm and points out the advantages and disadvantages in application.

Matching Islamic patterns in Kufic images

In this study, we address the problem of matching patterns in Kufic calligraphy images. Being used as a decorative element, Kufic images have been designed in a way that makes it difficult to be read by non-experts. Therefore, available methods for handwriting recognition are not easily applicable to the recognition of Kufic patterns. In this study, we propose two new methods for Kufic pattern matching. The first method approximates the contours of connected components into lines and then utilizes chain code representation. Sequence matching techniques with a penalty for gaps are exploited for handling the variations between different instances of sub-patterns. In the second method, skeletons of connected components are represented as a graph where junction and end points are considered as nodes. Graph isomorphism techniques are then relaxed for partial graph matching. Methods are evaluated over a collection of 270 square Kufic images with 8,941 sub-patterns. Experimental results indicate that, besides retrieval and indexing of known patterns, our method also allows the discovery of new patterns.

Calligraphy and Klee’s Abstract Painting: A Study on Categorical Ambiguity

This study analyses the categorical ambiguity between visual appearances that belong in different categories, such as Oriental Calligraphy and certain Abstract Paintings selected from the works of Klee. In particular, the aim of our research was to identify whether there exist abstract features at the basis of purely visual configurations that determine the way in which they are categorized. Specifically, the intention was to determine whether, and to what extent, two artistic forms that display documented shared graphic and conceptual characteristics differ, or do not differ, visually, and whether certain features exist that identify them as a specific type of graphic work. The assumption that both categories had shared characteristics that made them graphically and conceptually similar and that resulted in categorical ambiguity, was confirmed. Moreover, the presence of some constituent features specific to one or the other was also confirmed. The results show that a Calligraphic Image and an Abstract Painting by Klee can be ‘exchanged’, but the tendency to confuse Calligraphy with an Abstract Painting is greater than that of confusing an Abstract Painting with Calligraphy.