Ancient Chinese Characters Research Articles

Mythologizing of characters: Chinese character archaeology evidence of ancient Chinese totems related to “Yi (一)” cognate

This article endeavors to elucidate the mechanisms by which vestiges of mythology become embedded in the morphology of Chinese characters. It represents a foundational study aimed at explicating how oral mythological traditions and archaic mythical cognitions manifest themselves through the concrete graphemic structures of Chinese characters. The research focuses on the analysis of the characters “Yi (一),” “Yi (壹),” “Yi (羽/能),” and “Yi (鳦),” which are cognates. Through this examination, the study demonstrates that these graphemes are reflective of ancient Chinese veneration of the gourd, as well as bear and bird totemic traditions. To substantiate these claims, the methodology of “Chinese character archaeology” is employed, which integrates etymological analysis of the characters themselves with traditional textual sources, archaeological findings, and anthropological data. This approach can be extended to encompass a diverse array of totems, including plants, dragons, tigers, and horses. The ancient Chinese characters that encapsulate these totemic traditions are of particular significance as they offer insights into primordial modes of thought that predate the genesis of philosophical literature. Furthermore, this research potentially elucidates, through graphemics, the hypothesis that Chinese ancient civilization originated simultaneously in various regions with distinct totemic traditions, gradually converging into a predominant civilization.

Semantic-enhanced graph neural network for named entity recognition in ancient Chinese books

Named entity recognition (NER) plays a crucial role in the extraction and utilization of knowledge of ancient Chinese books. However, the challenges of ancient Chinese NER not only originate from linguistic features such as the use of single characters and short sentences but are also exacerbated by the scarcity of training data. These factors together limit the capability of deep learning models, like BERT-CRF, in capturing the semantic representation of ancient Chinese characters. In this paper, we explore the semantic enhancement of NER in ancient Chinese books through the utilization of external knowledge. We propose a novel model based on Graph Neural Networks that integrates two different forms of external knowledge: dictionary-level and chapter-level information. Through the Graph Attention Mechanism (GAT), these external knowledge are effectively incorporated into the model’s input context. Our model is evaluated on the C_CLUE dataset, showing an improvement of 3.82% over the baseline BAC-CRF model. It also achieves the best score compared to several state-of-the-art dictionary-augmented models.

Research on the Ancient and Modern Chinese Characters Quoted from the Book of the Later Han (後漢書) in Cefu Yuangui (冊府元龜) —The Second in a Series of Studies on the Quotations in Cefu Yuangui

Research on the Ancient and Modern Chinese Characters Quoted from the Book of the Later Han (後漢書) in Cefu Yuangui (冊府元龜) —The Second in a Series of Studies on the Quotations in Cefu Yuangui

An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8

Ancient Chinese characters known as oracle bone inscriptions (OBIs) were inscribed on turtle shells and animal bones, and they boast a rich history dating back over 3600 years. The detection of OBIs is one of the most basic tasks in OBI research. The current research aimed to determine the precise location of OBIs with rubbing images. Given the low clarity, severe noise, and cracks in oracle bone inscriptions, the mainstream networks within the realm of deep learning possess low detection accuracy on the OBI detection dataset. To address this issue, this study analyzed the significant research progress in oracle bone script detection both domestically and internationally. Then, based on the YOLOv8 algorithm, according to the characteristics of OBI rubbing images, the algorithm was improved accordingly. The proposed algorithm added a small target detection head, modified the loss function, and embedded a CBAM. The results show that the improved model achieves an F-measure of 84.3%, surpassing the baseline model by approximately 1.8%.

‘손(手)’類 部首字 중심의 草書 字形 및 類似 字形에 관한 고찰

This paper studies the cursive script of characters such as 手(Hands), 攴(slaps), 攵(slaps), 支(supports), 又(hands), 爪(nails), 寸(short length), 厶(arms), 力(strength), 文(words), 戈(weapons) and compound characters.
 This paper compares these characters with similar shapes. The meaning and shape of ancient characters have influenced cursive writing. Just organize it briefly.
 Words(‘手’) express the shape of fingers and fists. The shape of cursive script is affected by this. The character(‘攴’, ‘攵’) represents the appearance of holding a stick in hand. ‘戈’ is a weapon used in war. So these words replaced each other in cursive script. The character(‘攴’) appears alternately with this character( ‘力’字, ‘又’字, ‘’字, ‘支’字, ‘弓’字, ‘攵’字, ‘文’字 ). One of these phenomena is that the characters are similar, which is why they appear in cursive script. This character(‘’字, ‘弓’字) is influenced by that character(‘乃’字, ‘’字, ‘’字) and appears in cursive script. The cursive script of this character(‘又’) has also been replaced by that character(‘寸’). Or multiple of these words(‘又’) have been weakened. The cursive strokes of this character(‘爪’) have weakened. Elaborate or express in two points.
 This word(‘寸’) has a curved tail and then narrates together with dots. Or change the position of the point to express it. This character(‘對’) divides the upper part of that character(‘丵’) into three dots according to the ancient script shape. This character(‘厶’) is an old version of this character(‘厷’). This character(‘厷’) appears with that character(‘九’). The reason is that(‘九’) in ancient Chinese characters, this character(‘九’) presents the shape of a human arm, and that character(‘肱’) also presents the shape of a bent arm, so it appears alternately. Because these characters have a common curved shape, which character(‘厷’) is used instead of this one(‘弓’) to express. You can see the appearance of ‘厶’, ‘厷’, ‘弓’, ‘’ from the cursive script. This word(‘力’) expresses human muscles and tendons. These words(攵, 攴, 力) related to hands or arms are mixed together for use. This character(‘文’) also has a similar shape to that character(‘攵’), so it will also appear in cursive script. According to these contents, the characters are influenced by similar characters, ancient characters, and variant characters, appearing in cursive script characters. Exploring similar or ancient character forms can provide a lot of information for distinguishing cursive script forms. By using it, it is possible to provide rich explanations for more glyphs. I hope this will be of great help in understanding the shapes of cursive script.

‘손(手)’類 部首字 중심의 草書 字形 및 類似 字形에 관한 고찰

This paper studies the cursive script of characters such as 手(Hands), 攴(slaps), 攵(slaps), 支(supports), 又(hands), 爪(nails), 寸(short length), 厶(arms), 力(strength), 文(words), 戈(weapons) and compound characters.
 This paper compares these characters with similar shapes. The meaning and shape of ancient characters have influenced cursive writing. Just organize it briefly.
 Words(‘手’) express the shape of fingers and fists. The shape of cursive script is affected by this. The character(‘攴’, ‘攵’) represents the appearance of holding a stick in hand. ‘戈’ is a weapon used in war. So these words replaced each other in cursive script. The character(‘攴’) appears alternately with this character( ‘力’字, ‘又’字, ‘’字, ‘支’字, ‘弓’字, ‘攵’字, ‘文’字 ). One of these phenomena is that the characters are similar, which is why they appear in cursive script. This character(‘’字, ‘弓’字) is influenced by that character(‘乃’字, ‘’字, ‘’字) and appears in cursive script. The cursive script of this character(‘又’) has also been replaced by that character(‘寸’). Or multiple of these words(‘又’) have been weakened. The cursive strokes of this character(‘爪’) have weakened. Elaborate or express in two points.
 This word(‘寸’) has a curved tail and then narrates together with dots. Or change the position of the point to express it. This character(‘對’) divides the upper part of that character(‘丵’) into three dots according to the ancient script shape. This character(‘厶’) is an old version of this character(‘厷’). This character(‘厷’) appears with that character(‘九’). The reason is that(‘九’) in ancient Chinese characters, this character(‘九’) presents the shape of a human arm, and that character(‘肱’) also presents the shape of a bent arm, so it appears alternately. Because these characters have a common curved shape, which character(‘厷’) is used instead of this one(‘弓’) to express. You can see the appearance of ‘厶’, ‘厷’, ‘弓’, ‘’ from the cursive script. This word(‘力’) expresses human muscles and tendons. These words(攵, 攴, 力) related to hands or arms are mixed together for use. This character(‘文’) also has a similar shape to that character(‘攵’), so it will also appear in cursive script. According to these contents, the characters are influenced by similar characters, ancient characters, and variant characters, appearing in cursive script characters. Exploring similar or ancient character forms can provide a lot of information for distinguishing cursive script forms. By using it, it is possible to provide rich explanations for more glyphs. I hope this will be of great help in understanding the shapes of cursive script.

Ancient Chinese Character Recognition with Improved Swin-Transformer and Flexible Data Enhancement Strategies.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.

An Analysis of the Pragmatic Characteristics of "Jiong" in the Internet Era

With the Internet and technological developments, innovative communication methods and emerging new media have spawned rich cultural symbols in various forms. At the same time, they have created conditions for the rebirth and return of some long-dormant ancient Chinese characters, and "Jiong ()" is one of them. Previous researchers have made some progress in the evolution of the semantic meaning of "Jiong" from ancient times to the Internet era and have obtained a relatively clear timeline. However, there is still a certain research gap in the academic circle on the specific pragmatic use of "Jiong" in the Internet era and how its pragmatic use is affected by a variety of factors in the Internet era. Therefore, from the perspective of pragmatics, this study first analyzes the usages of "Jiong" in different contexts with specific examples and then uses data statistics and theoretical analysis to finally find that the pragmatics of "Jiong" are influenced by the network context, the homophone "embarrassment ()", the Chinese pinyin of which is also "Jiong", and the mass media.

An unsupervised automatic organization method for Professor Shirakawa’s hand-notated documents of oracle bone inscriptions

As one of the most influential Chinese cultural researchers in the second half of the twentieth-century, Professor Shirakawa is active in the research field of ancient Chinese characters. He has left behind many valuable research documents, especially his hand-notated oracle bone inscriptions (OBIs) documents. OBIs are one of the world’s oldest characters and were used in the Shang Dynasty about 3600 years ago for divination and recording events. The organization of OBIs is not only helpful in better understanding Prof. Shirakawa’s research and further study of OBIs in general and their importance in ancient Chinese history. This paper proposes an unsupervised automatic organization method to organize Prof. Shirakawa’s OBIs and construct a handwritten OBIs data set for neural network learning. First, a suite of noise reduction is proposed to remove strangely shaped noise to reduce the data loss of OBIs. Secondly, a novel segmentation method based on the supervised classification of OBIs regions is proposed to reduce adverse effects between characters for more accurate OBIs segmentation. Thirdly, a unique unsupervised clustering method is proposed to classify the segmented characters. Finally, all the same characters in the hand-notated OBIs documents are organized together. The evaluation results show that noise reduction has been proposed to remove noises with an accuracy of 97.85%, which contains number information and closed-loop-like edges in the dataset. In addition, the accuracy of supervised classification of OBIs regions based on our model achieves 85.50%, which is higher than eight state-of-the-art deep learning models, and a particular preprocessing method we proposed improves the classification accuracy by nearly 11.50%. The accuracy of OBIs clustering based on supervised classification achieves 74.91%. These results demonstrate the effectiveness of our proposed unsupervised automatic organization of Prof. Shirakawa’s hand-notated OBIs documents. The code and datasets are available at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.

Large-scale continual learning for ancient Chinese character recognition

Ancient Chinese character recognition is a challenging problem in the field of pattern recognition. It is difficult to collect all character classes during the training stage due to the numerous classes of ancient Chinese characters and the likelihood of discovering new characters over time. A solution to address this problem is continual learning. However, most continual learning methods are not well-suited for large-scale applications, making them insufficient for solving the problem of ancient Chinese character recognition. Although saving raw data for old classes is a good approach for continual learning to address large-scale problems, it is often infeasible due to the lack of data accessibility in reality. To solve these problems, we propose a large-scale continual learning framework based on the convolutional prototype network (CPN), which does not save raw data for old classes. In this paper, several basic strategies have been proposed for the initial training stage to enhance the feature extraction ability and robustness of the network, which can improve the performance of the model in continual learning. In addition, we propose two practical methods in varying feature space (parameters of feature extractor are changeable) and fixed feature space (parameters of feature extractor are fixed), which enable the model to carry out large-scale continual learning. The proposed method does not save the raw data of old classes and enables simultaneous classification of all existing classes without knowing the incremental batch number. Experiments on the CASIA-AHCDB dataset with 5000 character classes demonstrate the effectiveness and superiority of the proposed method.

Han Meilin’s style path of creating ancient Chinese character art——Taking “TianShu” as an example

Han Meilin pursued the “beauty of lines, composition, and form” in the genes of ancient Chinese characters through modern “prioritization operation, flexible combination, and two-dimensional and three-dimensional cross-border”. He created a series of works in both Chinese and modern “Tianshu” style, and held an art exhibition in the main building of the Meridian Gate of the Forbidden City. It can be seen that Han Meilin’s style of ancient Chinese characters conforms to the modern aesthetic of Chinese style in the context of contemporary art and design. The article establishes a connection between the formation path of artist style and personality power from the perspective of subjective anthropology, extracts the method of Han Meilin’s transformation of ancient Chinese characters genes, and provides a style path that can be referenced for the development of national art resources in modernity.

SideNet: Learning representations from interactive side information for zero-shot Chinese character recognition

Existing methods for zero-shot Chinese character recognition usually exploit a single type of side information such as radicals, glyphs, or strokes to establish a mapping with the input characters for the recognition of unseen categories. However, these approaches have two limitations. Firstly, the mappings are inefficient owing to their complexity. Some existing methods design radical-level mappings using a non-differentiable dictionary-matching strategy, whereas others construct sophisticated embeddings to map seen and unseen characters into a unified latent space. Although the latter approach is straightforward, it lacks a learnable scheme for explicit structure construction. Secondly, the complementarity within multiple types of side information has not been effectively explored. For example, the radicals provide structural knowledge at an abstract level, whereas glyphs offer detailed information on their figurative counterparts. To this end, we propose a new method called SideNet that jointly learns character-level representations assisted by two types of interactive side information: radicals and glyphs. SideNet contains a structural conversion module that extracts radical knowledge via dimensional decomposition, and a spatial conversion module that encodes the radical counting map to produce an interactive outcome between radicals and glyph. Finally, we propose a new classifier that integrates the converted features by a similarity-guided fusion mechanism. To the best of our knowledge, this study represents the first attempt to integrate these two types of side information and explore a joint representation for zero-shot learning. Experiments show that SideNet consistently outperforms existing methods by a significant margin in diverse scenarios, including handwriting, printed art, natural scenes, and ancient Chinese characters, which demonstrates the potential of joint learning with multiple types of side information.

Interpreting the Egyptian Code in Nineteenth-Century China: Pan Zuyin and His Circle of Antiquarians

Abstract Antiquarianism in China experienced a dramatic change in the second half of the nineteenth century. While ancient Chinese artifacts were still eagerly sought after, scholars began to collect Egyptian inscriptions. Unequipped with any knowledge of the Egyptian writing system, they were fascinated by the stylistic similarity between Egyptian hieroglyphs and ancient Chinese seal-script characters. This article centers on the scholar-official Pan Zuyin (1830–90) and his collection of ink rubbings of the Decree of Canopus, a stele with trilingual inscriptions carved in the Ptolemaic era. It examines how Qing antiquarians interpreted ancient Egyptian inscriptions, adapting, denying, and transforming Champollion’s approach by explaining the formation and evolution of the Egyptian writing system in terms of their own philological theory, the liushu (the six modes of character formation). It argues that the craze for Egyptian inscriptions resulted from discursive changes occurring in the field of antiquarian study as the scope of acceptable research areas was broadened and the strange and exotic objects—previously considered uncollectible—began to attract scholarly attention. The popularity of Egyptian inscriptions also demonstrates that serious philological study and an amateurish pleasure-seeking attitude were not so much oppositional binaries as interdependent activities.

Attention-based deformable convolutional network for Chinese various dynasties character recognition

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually evolving into modern Chinese characters. However, because of the intrinsic limitations of traditional convolutional networks in the model, the geometric structure of the convolutional module is fixed. Therefore, we build a new architecture called attention-based deformable networks for this task. Besides, we construct a new dataset called the Chinese characters from Various Dynasties Dataset (CCDD), which includes the evolution of Chinese characters between major dynasties. Deformable convolution based on attention mechanism can obtain those more important offsets, resulting in better performance than the original deformable convolution. In the experimental stage, extensive experiments validate the performance of our approach. Firstly, the effectiveness of this network was verified on two text datasets, Mnist(99.38%) and notMnist(98.22%) both comparing with several modules. Finally, the best performance was achieved on the CCDD dataset(91.50%) compared to other classical deep learning networks. This is the first paper to apply deep learning methods to recognize and classify the characters of major dynasties in China. And archaeological workers need to be able to recognize Chinese characters from various periods in China, and our algorithm can assist them in completing this task to a certain extent. It can also help cultural relic enthusiasts roughly determine the age of cultural relics.

The Evolution of Semantic Domain that Both Noun and Verb’s Distinction from the Perspective of the Ancient Chinese Character “Du (度)”

The Evolution of Semantic Domain that Both Noun and Verb’s Distinction from the Perspective of the Ancient Chinese Character “Du (度)”

Retracted: Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis.

[This retracts the article DOI: 10.1155/2021/2491116.].

On the Language Preferences of Chinese Leaders

For centuries. attention to language problems in China has been paid at the highest level. In his speech at the 20th CPC Congress, Xi Jinping mentioned the promotion of the “all-national language and script”, i.e. Standard Mandarin and Chinese characters, as an important part of the language policy. Propaganda of traditional culture and preservation of ancient written texts is another important task in modern China. It is not by chance that soon after the 20th Congress, Xi Jinping visited Yinxu, a museum-park on the site of the second capital of the Shang-Yin dynasty, where the oldest jiaguwen character inscriptions had been discovered at the end of the 19th century. In the epoch of the revival of philological traditions, Xi Jinping’s signature in his own calligraphic version with the traditional character 習xí is widely used in Chinese propaganda. Soon after the 20th Congress, the importance of the popularization of the Chinese language and culture outside the country was discussed in Xi Jinping’s letter sent to a group of students from Saudi Arabia. As for the romanization system Hanyu Pinyin, introduced in the PRC in 1958, in the official documents it is positioned as an auxiliary device used in the primary education in China, when writing Chinese words in the texts in foreign languages and in the process of teaching Chinese to foreigners or not mentioned at all. The status of the Hanyu Pinyin system in modern China is incomparable with the status of the ancient Chinese characters.

Consideration of a text encoding initiative markup for ancient Chinese characters present on Chinese excavated materials

Consideration of a text encoding initiative markup for ancient Chinese characters present on Chinese excavated materials

진변한 24국 위치 비정

Some records, including the record of the Han(韓傳) in the Samkukji(三國志), lists the national names of the 24 countries of Jinhan(辰韓) and Byeonhan(弁韓). The records show the reality of the countries that first occurred in Gyeongsang-Do today. In this respect, it is very basic and important historically to determine where the countries were located. This work h as b een attempted by several s cholars, but opinions a re o ften d ifferent f or e ach scholar, and not much research has been conducted recently. This article reviewed the locations of all the 24 countries of Jinhan and Byeonhan. First, the author grasped the meaning of the order in which the names of the countries were recorded and the scope of the country's location, and reviewed the relics of the Samhan (三韓) period in Gyeongsang-Do, which can be said to be traces of these countries. After that, identified the pronunciation of the national name at that time through research on Chinese character phonology and compared it with the place names recorded in the Samguksagi (三國史記). If further research on the pronunciation of ancient Chinese characters is conducted and more relics from the Samhan Period are found, the opinion could be revised. In this respect, it is only a result made at today's academic stage. However, this study is meaningful in that it attempted to determine the location of the 24 countries of Jinhan and Byeonhan based on archaeological excavations conducted until recently and the achievements of Chinese character phonology in Chinese academia.

Research on denoising method of chinese ancient character image based on chinese character writing standard model

Ancient documents are historical evidence of cultural inheritance, and the damage brought by natural and human factors to ancient documents is inevitable, resulting in the collected images of ancient Chinese characters containing a large amount of noise, which seriously affects the accuracy of subsequent image recognition and thus creates a great obstacle to the digitization of ancient documents. To address the complexity of ancient text structure, this paper proposes a Chinese ancient text image denoising method based on the Chinese character writing standard model. The method firstly adds four additional local branches based on the global branching, and uses the supplementary character detail information to weaken the phenomenon of strokes adhering to noise due to the lack of local details; secondly, it introduces the simulation noise of ancient documents to simulate the real ancient character image morphology, which can be used for the adversarial training of this method. In the training process, the minimum absolute value deviation, smoothing loss, structural consistency loss and the refined loss function formed by the adversarial loss are used to iteratively optimize the parameters. Finally, experiments prove that the model in this paper can increase the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the image by at least 23.8% and 11.4%, and the user evaluation index (UV) has also reached more than 80%.