1,072 research outputs found
Handwriting Beautification
katedra kybernetik
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Degraded images commonly exist in the general sources of character images,
leading to unsatisfactory character recognition results. Existing methods have
dedicated efforts to restoring degraded character images. However, the
denoising results obtained by these methods do not appear to improve character
recognition performance. This is mainly because current methods only focus on
pixel-level information and ignore critical features of a character, such as
its glyph, resulting in character-glyph damage during the denoising process. In
this paper, we introduce a novel generic framework based on glyph fusion and
attention mechanisms, i.e., CharFormer, for precisely recovering character
images without changing their inherent glyphs. Unlike existing frameworks,
CharFormer introduces a parallel target task for capturing additional
information and injecting it into the image denoising backbone, which will
maintain the consistency of character glyphs during character image denoising.
Moreover, we utilize attention-based networks for global-local feature
interaction, which will help to deal with blind denoising and enhance denoising
performance. We compare CharFormer with state-of-the-art methods on multiple
datasets. The experimental results show the superiority of CharFormer
quantitatively and qualitatively.Comment: Accepted by ACM MM 202
Generating Handwritten Chinese Characters using CycleGAN
Handwriting of Chinese has long been an important skill in East Asia.
However, automatic generation of handwritten Chinese characters poses a great
challenge due to the large number of characters. Various machine learning
techniques have been used to recognize Chinese characters, but few works have
studied the handwritten Chinese character generation problem, especially with
unpaired training data. In this work, we formulate the Chinese handwritten
character generation as a problem that learns a mapping from an existing
printed font to a personalized handwritten style. We further propose DenseNet
CycleGAN to generate Chinese handwritten characters. Our method is applied not
only to commonly used Chinese characters but also to calligraphy work with
aesthetic values. Furthermore, we propose content accuracy and style
discrepancy as the evaluation metrics to assess the quality of the handwritten
characters generated. We then use our proposed metrics to evaluate the
generated characters from CASIA dataset as well as our newly introduced Lanting
calligraphy dataset.Comment: Accepted at WACV 201
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
Constructing high-quality character image datasets is challenging because
real-world images are often affected by image degradation. There are
limitations when applying current image restoration methods to such real-world
character images, since (i) the categories of noise in character images are
different from those in general images; (ii) real-world character images
usually contain more complex image degradation, e.g., mixed noise at different
noise levels. To address these problems, we propose a real-world character
restoration network (RCRN) to effectively restore degraded character images,
where character skeleton information and scale-ensemble feature extraction are
utilized to obtain better restoration performance. The proposed method consists
of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet
aims to preserve the structural consistency of the character and normalize
complex noise. Then, CiRNet reconstructs clean images from degraded character
images and their skeletons. Due to the lack of benchmarks for real-world
character image restoration, we constructed a dataset containing 1,606
character images with real-world degradation to evaluate the validity of the
proposed method. The experimental results demonstrate that RCRN outperforms
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202
Chinese calligraphy: character style recognition based on full-page document
Calligraphy plays a very important role in the history of China. From ancient times to
modern times, the beauty of calligraphy has been passed down to the present. Different
calligraphy styles and structures have made calligraphy a beauty and embodiment in the
field of writing. However, the recognition of calligraphy style and fonts has always been
a blank in the computer field. The structural complexity of different calligraphy also
brings a lot of challenges to the recognition technology of computers. In my research, I
mainly discussed some of the main recognition techniques and some popular machine
learning algorithms in this field for more than 20 years, trying to find a new method of
Chinese calligraphy styles recognition and exploring its feasibility.
In our research, we searched for research papers 20 years ago. Most of the results are
about the content recognition of modern Chinese characters. At first, we analyze the
development of Chinese characters and the basic Chinese character theory. In the
analysis of the current recognition of Chinese characters (including handwriting online
and offline) in the computer field, it is more important to analyze various algorithms
and results, and to analyze how to use the experimental data, besides how they construct
the data set used for their test.
The research on the method of image processing based on Chinese calligraphy works
is very limited, and the data collection for calligraphy test is very limited also. The test
of dataset that used between different recognition technologies is also very different.
However, it has far-reaching significance for inheriting and carrying forward the
traditional Chinese culture. It is very necessary to develop and promote the recognition
of Chinese characters by means of computer tecnchque. In the current application field,
the font recognition of Chinese calligraphy can effectively help the library
administrators to identify the problem of the classification of the copybook, thus
avoiding the recognition of the calligraphy font which is difficult to perform manually
only through subjective experience.
In the past 10 years of technology, some techniques for the recognition of single
Chinese calligraphy fonts have been given. Most of them are the pre-processing of
calligraphy characters, the extraction of stroke primitives, the extraction of style
features, and the final classification of machine learning. The probability of the
classification of the calligraphy works. Such technical requirements are very large for
complex Chinese characters, the result of splitting and recognition is very large, and it
is difficult to accurately divide many complex font results. As a result, the recognition
rate is low, or the accuracy of recognition of a specific word is high, but the overall font
recognition accuracy is low.
We understand that Chinese calligraphy is a certain research value. In the field of
recognition, many research papers on the analysis of Chinese calligraphy are based on
the study of calligraphy and stroke. However, we have proposed a new method for
dealing with font recognition. The recognition technology is based on the whole page
of the document. It is studied in three steps: the first step is to use Fourier transform and
some Chinese calligraphy images and analyze the results. The second is that CNN is
based on different data sets to get some results. Finally, we made some improvements
to the CNN structure. The experimental results of the thesis show that the full-page
documents recognition method proposed can achieve high accuracy with the support of
CNN technology, and can effectively identify the different styles of Chinese calligraphy
in 5 styles. Compared with the traditional analysis methods, our experimental results
show that the method based on the full-page document is feasible, avoiding the
cumbersome font segmentation problem. This is more efficient and more accurate
Research on Calligraphy Evaluation Technology Based on Deep Learning
Today, when computer-assisted instruction (CAI) is booming, related research in the field of calligraphy education still hasn’t much progress. This main research for the calligraphy beginners to evaluate their works anytime and anywhere. Author uses the literature research and interview to understand the common writing problems of beginners. Then conducts discussion on these problems, design of solutions, research on algorithms, and experimental verification. Based on the ResNet-50 model, through WeChat applet implements for beginners. The main research contents are as follows:
(1) In order to achieve good results in calligraphy judgment, this article uses the ResNet-50 model to judge calligraphy. First, adjust the area of the handwritten calligraphy image as the input of the network to a small block suitable for the network. While training the network, adjust the learning rate, the number of image layers and the number of training samples to achieve the optimal. The research results show that ResNet has certain practicality and reference value in the field of calligraphy judgment. Regarding the possible over-fitting problem, this article proposes to improve the accuracy of the judgment by collecting more data and optimizing the data washing process.
(2) Combining the rise of WeChat applets, in view of the current WeChat applet learning platform development process and the problem of fewer functional modules, this paper uses cloud development functions to develop a calligraphy learning platform based on WeChat applets. While simplifying the development process, it ensures that the functional modules of the platform meet the needs of teachers and beginners, it has certain practicality and commercial value. After the development of the calligraphy learning applet is completed, it will be submitted for official
A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution
The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing quality evaluation subsystem assesses written strokes using a convolutional auto-encoder network (CAE), which enables the generation of aesthetic strokes with various writing styles. The trained CAE network effectively excludes poorly written strokes through stroke reconstruction, but guarantees the inheritance of information from well-written ones. With the support of the evaluation subsystem, the writing trajectory model generation subsystem is realized by multivariate normal distributions optimized by differential evolution (DE), a type of heuristic optimization search algorithm. The proposed approach was validated and evaluated using a dataset of nine stroke categories; high-quality written strokes have been resulted with good diversity which shows the robustness and efficacy of the proposed approach and its potential in autonomous action-state space exploration for other real-world applications
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