The Inheritance and Application of Chinese Reverse Contrast Typeface Style

This paper discusses the inheritance and application of Chinese character reverse contrast typeface style.It begins by analyzing the visual features of Western reverse contrast typeface styles,with a focus on Caslon Italian and French Clarendon,providing a Western perspective reference for subsequent Chinese character reverse contrast typeface style designs.The paper then traces the origins of the Chinese reverse contrast style,from the calligraphy style"Lacquer Script"to the earliest printing type"フワンテール形",exploring the historical background and cultural significance of the Chinese reverse contrast style.In the methodology section of Chinese character reverse contrast typeface style design,the discussion is conducted from two dimensions:inheritance and application.In terms of inheritance,through an in-depth analysis of"Lacquer Script"and"フワンテール形"typeface style,the paper summarizes three basic theories for modern Chinese character reverse contrast typeface style design.In the application section,it examines in detail the two most influential recent typeface styles,"Ribaasu"and"Basic Artistic",outlining three directions of application:extreme horizontal stroke variations,exaggerated contrast,and diverse decorative strokes,showcasing new directions and possibilities for Chinese character reverse contrast typeface style design.This paper not only reviews the developmental history of the Chinese character reverse contrast typeface style but also analyzes the design methodology of Chinese character reverse contrast typeface style through specific case studies.

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions.

Discussion of Online Teaching Strategies for Teaching Elementary Chinese Characters to Foreigners Based on Error Analyses

The expanding role of the Chinese language in international communications has become increasingly prominent as China’s comprehensive national power continues to grow,leading to a significant rise in the number of Chinese language learners.Since online teaching is not limited by time and space,its application is widespread.For beginners in the Chinese language,the Chinese characters are both a priority and a challenge.The“Chinese Character Classification,”also known as the“Six Writings,”is the earliest systematic theory of Chinese character structures,and teaching Chinese characters in categories based on the“Chinese Character Classification”is a method that fits the cognition of beginners.In order to teach Chinese characters in a targeted approach,based on the collection and analysis of the common errors of Chinese characters among beginners,(1)this paper proposes that(a)the intuitive method can be applied to teach pictographic characters,indicative characters,and associative compound characters in online teaching;(b)the inductive-deductive method of“basic characters to new characters”can be applied for the teaching of pictophonetic characters and associative compound characters;(c)the learning of character patterns should be approached in a whole-part-whole process,while importance should be attached to the suggestion of the frequency effect with a view to facilitating the online learning of Chinese characters for beginners.The aim of this paper is to provide some practical implications for the online teaching of Chinese characters to foreigners.

Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph

In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.

The comparison between traditional Chinese literacy strategies and English vocabulary strategies

There is a positive transfer from native language vocabulary learning strategy to that of the second language. The comparison between them shows that the traditional Chinese character learning strategies have profound effect on English vocabulary learning on the basis of morphology, lexicon as well as discourse categories. If the mutual effect can be applied in English vocabulary learning effectively, positive transfer emerges.

Chinese word segmentation with local and global context representation learning

A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.

An on-line free handwritten Chinese character recognition method based on component cascaded HMMs

This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.

Processing Chinese hand-radicals activates the medial frontal gyrus A functional MRI investigation

Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studies supporting this theory have focused on Chinese characters, less attention has been paid to their semantic radicals. Indeed, there is still disagreement about whether these radicals are processed independently. The present study investigated whether radicals are processed separately and, if so, whether this processing occurs in sensory-motor regions. Materials consisted of 72 high-frequency Chinese characters, with 18 in each of four categories： hand-action verbs with and without hand-radicals, and verbs not related to hand actions, with and without hand-radicals. Twenty-eight participants underwent functional MRI scans while reading the characters. Compared to characters without hand-radicals, reading characters with hand-radicals activated the right medial frontal gyrus. Verbs involving hand-action activated the left inferior parietal lobule, possibly reflecting integration of information in the radical with the semantic meaning of the verb. The findings may be consistent with embodied semantics theory and suggest that neural representation of radicals is indispensable in processing Chinese characters.

Research on Handwritten Chinese Character Recognition Based on BP Neural Network

The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before.Handwritten Chinese character recognition,as a hot research object in image pattern recognition,has many applications in people’s daily life,and more and more scholars are beginning to study off-line handwritten Chinese character recognition.This paper mainly studies the recognition of handwritten Chinese characters by BP(Back Propagation)neural network.Establish a handwritten Chinese character recognition model based on BP neural network,and then verify the accuracy and feasibility of the neural network through GUI(Graphical User Interface)model established by Matlab.This paper mainly includes the following aspects:Firstly,the preprocessing process of handwritten Chinese character recognition in this paper is analyzed.Among them,image preprocessing mainly includes six processes:graying,binarization,smoothing and denoising,character segmentation,histogram equalization and normalization.Secondly,through the comparative selection of feature extraction methods for handwritten Chinese characters,and through the comparative analysis of the results of three different feature extraction methods,the most suitable feature extraction method for this paper is found.Finally,it is the application of BP neural network in handwritten Chinese character recognition.The establishment,training process and parameter selection of BP neural network are described in detail.The simulation software platform chosen in this paper is Matlab,and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition.Design the GUI interface of human-computer interaction based on Matlab,show the process and results of handwritten Chinese character recognition,and analyze the experimental results.

An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration

This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.

Information Moment for Chinese Character Recognition

Moment invariants firstly introduced by M. K Hu in 1962, has some shortcomings. After counting a large number of statistical distribution information of Chinese characters,the authors put forward the concept of information moments and demonstrate its invariance to translation,rotation and scaling.Also they perform the experiment in which information moments compared with moment invaiants for the effects of similar Chinese characters and font recognition.At last they show the recognition rate of 88% by information moments,with 70% by moment inariants.

Using Neuroeducation as a Model to Evaluate the Effect of Imagery on Chinese Character Writing

Using imagery as a strategy for language learning may be helpful to encode linguistic forms into conceptual networks for long-term memory. Based on Arwood＇s neuroeducation model of language learning, this research evaluated the effect of imagery in Chinese character writing by English-speaking adolescent students. After comparing imagery effects under three instructional conditions （i.e., English translation, pictorial presentation, and verbal-contextual interpretation）, the results showed that the use of imagery predicted significantly better writing results in the immediate and one-week writing tests, but not in the four-week writing test. Cognitive analyses found that imagery was commonly used as a mediational strategy in the pictorial and verbal-contextual methods in the early learning phases. The pictorial method mainly elicited perceptual visual patterns which failed to support sustained memory. For a better character encoding and retrieval, images had to be generated associated with sufficient and relevant contextual information.

CTSF:An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

The past decade has seen the rapid development of text detection based on deep learning.However,current methods of Chinese character detection and recognition have proven to be poor.The accuracy of segmenting text boxes in natural scenes is not impressive.The reasons for this strait can be summarized into two points:the complexity of natural scenes and numerous types of Chinese characters.In response to these problems,we proposed a lightweight neural network architecture named CTSF.It consists of two modules,one is a text detection network that combines CTPN and the image feature extraction modules of PVANet,named CDSE.The other is a literacy network based on spatial pyramid pool and fusion of Chinese character skeleton features named SPPCNN-SF,so as to realize the text detection and recognition,respectively.Our model performs much better than the original model on ICDAR2011 and ICDAR2013(achieved 85%and 88%F-measures)and enhanced the processing speed in training phase.In addition,our method achieves extremely performance on three Chinese datasets,with accuracy of 95.12%,95.56%and 96.01%.

A Bit Progress on Word-Based Language Model

A good language model is essential to a postprocessing algorithm for recognition systems. In the past, researchers have presented various language models, such as character based language models, word based language model, syntactical rules language model, hybrid models, etc . The word N gram model is by far an effective and efficient model, but one has to address the problem of data sparseness in establishing the model. Katz and Kneser et al. respectively presented effective remedies to solve this challenging problem. In this study, we proposed an improvement to their methods by incorporating Chinese language specific information or Chinese word class information into the system.

Selectivity of N170 in the left hemisphere as an electrophysiological marker for expertise in reading Chinese

Objective The left-lateralized N170, an event-related potential component consistently shown in response to alphabetic words, is a robust electrophysiological marker for reading expertise in an alphabetic language. In contrast, such a marker is lacking for expertise in reading Chinese, because the existing results about the lateralization of N170 for Chinese characters are mixed, reflecting complicated factors such as top-down modulation that contribute to the relative magnitudes of N170 in the left and right hemispheres. The present study aimed to explore a potential electrophysiological marker for reading expertise in Chinese with minimal top-down influence. Methods We recorded N170 responses to Chinese characters and three kinds of control stimuli in a content-irrelevant task, minimizing potential top-down effects. Results Direct comparison of the N170 amplitude in response to Chinese characters between the hemispheres showed a marginally significant left-lateralization effect. However, detailed analyses of N170 in each hemisphere revealed a more robust pattern of left-lateralization - the N170 in the left but not the right hemisphere differentiated Chinese characters from control stimuli. Conclusion These results suggest that the selectivity of N170 （a greater N170 in response to Chinese characters than to control stimuli） within the left hemisphere rather than the hemispheric difference of N170 with regard to Chinese characters is an electrophysiological marker for expertise in reading Chinese.

Parallel compact integration in handwritten Chinese character recognition

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.

Lu Xun＇s View of the Awakening of the Chinese People--Was There Really an ＂Epistemological Break＂？

It has been argued that, starting in the late 1920s, Lu Xun＇s intellectual development underwent a significant transformation constituting what the French Marxist philosopher Louis Althusser has termed an ＂epistemological break.＂ Some of the explicitly more positive comments about the masses that Lu Xun made in his later years have been used to demonstrate this point. However, the existence of such a ＂break＂ is still debatable, and a detailed examination of Lu Xun＇s apparently optimistic comments reveals that Lu Xun possessed a more sophisticated understanding of the masses and the Chinese people. His understanding was informed by the concept of ＂national character.＂ This paper attempts to demonstrate the consistency of Lu Xun＇s view of the masses and the Chinese people and to resolve an apparent self-contradiction in Lu Xun＇s arguments.

A New Linguistic Decoding Method for Online Handwritten Chinese Character Recognition

This paper presents a new linguistic decoding method for online handwritten Chinese character recognition. The method employs a hybrid language model which combines N-gram and linguistic rules by rule quantification technique. The linguistic decoding algorithm consists of three stages: word lattice construction, the optimal sentence hypothesis search and self-adaptive learning mechanism. The technique has been applied to palmtop computer's online handwritten Chinese character recognition. Samples containing millions of characters were used to test the linguistic decoder. In the open experiment, accuracy rate up to 92% is achieved, and the error rate is reduced by 68%.

ISOLATION, CHARACTERIZATION AND MEDICAL PROPERTIES OF ANTICOMPLEMENT AGENTS FROM TRADITIONAL CHINESE MEDICINES

The inappropriate activation of complement system may cause some life-threatening symptoms such as rheumatoid arthritis,systemic lupus erythematosus(SLE)and acute respiratory distress syndrome(ARDS).In our efforts to obtain natural anticomplement agents from traditional Chinese medicines(TCMs)for prevention and treatment of the complement-associated diseases。

Feature Extraction of Chinese Characters Based on ASM Algorithm

In Chinese Calligraphy education,the computer-based evaluation on Chinese handwriting is one of the problems in the field of computer intelligent education.In this study,the method of feature comparison is first proposed in the process of computer-based evaluation on Chinese handwriting,focusing on automatically and accurately extracting the features of Chinese characters.Then,the key technologies applied in feature extraction of Chinese character were analyzed.It discussed the representation of features,aligns training samples,and reduces dimensions by principal component analysis,established local grayscale model,and converged the gray-scale information of target feature points through statistical analysis.The experimental results show that the accuracy of the algorithm is 93.84%.