Ancient Chinese Glyph Identification Powered by Radical Semantics (2024)

Abstract: The ancestor of Chinese character – the ancient characters from about 1300 BC to 200 BC are not fixed in their writing glyphs. At the same or different points in time, one character can possess multiple glyphs that are different in shapes or radicals. Nearly half of ancient glyphs have not been deciphered yet. This paper proposes an innovative task of ancient Chinese glyph identification, which aims at inferring the Chinese character label for the unknown ancient Chinese glyphs which are not in the training set based on the image and radical information. Specifically, we construct a Chinese glyph knowledge graph (CGKG) associating glyphs in different historical periods according to the radical semantics, and propose a multimodal Chinese glyph identification framework (MCGI) fusing the visual, textual, and the graph data. The experiment is designed on a real Chinese glyph dataset spanning over 1000 years, it demonstrates the effectiveness of our method, and reports the potentials of each modality on this task. It provides a preliminary reference for the automatic ancient Chinese character deciphering at the glyph level.

heading: 1 Introduction
text: The ancient Chinese characters before 200 BC are largely different from the modern ones, which mainly include the Oracle bone script (Oracle) in about 1300 BC (Boltz, 1986), the Chinese bronze script (Bronze, about 1000 BC) (Shaughnessy, 1991) and the script belonging to the Warring States period (States, about 400 BC) (Qiu, 2014).
The glyphs of ancient character are not fixed. Figure 1 shows 17 glyphs of the character “春” (spring) distributed in 5 time stages of evolution (Oracle, Bronze, States, Small Seal and Clerical). Although they look different, there are potential semantic relations between their radicals: their radical systems both express the “spring” meaning by
∗* Corresponding authors
portraying a picture of “plants are growing under the sun”. Thus, many of the glyphs contain semantic radicals of “屮” (grass), “木” (tree) and “日” (sun), and nearly all of glyphs contain the phonetic radical “屯” (“屯” and “春” are homophones).
At present, about half of the ancient glyphs have not been deciphered, which means that we do not know the modern Chinese characters they corresponded, and can not completely interpret the historical documents containing them. Experts have to compare all related glyphs in history for reasoning. However, there are tens of thousands of glyphs distributed in various time stages with complex relationships between their radicals and shapes, which makes deciphering a difficult work that heavily relies on human memory and experience.
Please imagine a real scenario: given a unknown ancient Chinese glyph, we can obtain its images and radical information, and learn the radical semantics and potential usage patterns from the known glyphs in history. Then, can we automatically speculate the Chinese character to which the unknown glyph belongs? And to what extent the
12065
visual and radical information can support this task respectively? Answering these questions has significance for ancient Chinese character deciphering.
In this background, our contributions include: (1) we propose an innovative task of ancient Chinese glyph identification, to infer the Chinese character label for unknown glyphs in training set based on the image and radical information (Figure 2); (2) we devise a radical semantics powered multimodal method, including constructing a Chinese glyph knowledge graph (CGKG) and proposing a glyph identification framework (MCGI) fusing the visual, textual and graph features; (3) we evaluate the method on a glyph dataset spanning over 1000 years in both synchronic and diachronic views. It proves the validity of our method compared with the baselines, and gives the conclusion of the contributions for each modality to this task.
This paper provides a preliminary reference for the ancient character deciphering. It has potential applications in ancient Chinese, ancient character, history, and other related fields. For instance, to help experts discover the top-k possible character labels for the unknown glyph, as well as their relevant glyphs in history; and to improve the effects of optical character recognization through radical semantics.
The organization of this paper is as follows: Section 2 presents the related works; Section 3 describes the key knowledge and the task definition; Section 4 introduces CGKG; Section 5 introduces our glyph identification framework (MCGI); the evaluation is proposed in Section 6; Section 7 describes the limitations and future works; and Section 8 concludes this paper.

heading: 2 Related Works
text: Related works on Chinese characters: Most of the related works concentrated on optical character recognition (OCR) (Cao et al., 2020; Diao et al., 2023; Huang et al., 2022; Zhang et al., 2018). However, different from them, our focus is not on image recognition, but to identify the unknown ancient glyphs based on image and radical semantics. Chi et al. (2022) introduced an ancient Chinese glyph similarity measurement method that extracted radical semantic features from the graph, but the method is not designed for glyph identification. Zhang et al. (2021) presented an unknown image as a query, to receive a set of similar images from an adjacent writing system with associated
scholarly information, and so help guide the deciphering of the query. Chang et al. (2022) presented an image-to-image translation networks to generate corresponding modern Chinese forms by simulating and restoring the real historical evolutionary process of Oracle characters, which enables archaeologists to use the generated modern characters to infer possible lexical natures of Oracle characters. Although these works also can serve for deciphering, the specific tasks are not identical with ours. And compared with these works, because of introducing radical semantics, our method is more suitable for identifying the unknown glyphs who have different radical composition to their corresponding modern glyph or other variant glyphs. It is consistent with the theory of human deciphering: in the deciphering works, experts not only need to compare with visual similar glyphs but also discover and reason those glyphs that are related in semantics and pronunciations based on the radicals.
Multimodal Methods: the related works are distributed in domains of vision-and-language models (Kim et al., 2021; Radford et al., 2021; Xu et al., 2023), NLP and multimodal knowledge graph representation (Li et al., 2023; Wang et al., 2022; Zhang et al., 2023). Most of the multimodal models follow an universal workflow: generating the embedding for each modality through the pre-trained uni-modal encoders, such as BERT (Devlin et al., 2019), the Deep Residual Network (ResNet) (He et al., 2016), and the Graph Attention Network (GAT)(Velickovic et al., 2018), respectively for textual, visual and graph modality. These embeddings will be fed into a cross-modal encoder for data fusion, which can be a simply dot product operation, multimodal attention mechanisms or more complicated transformers. ViLT (Kim et al., 2021) took shallow embedding layers and used transformer on textual and visual embedding interactions. In the knowledge graph domain, HRGAT (Wang et al., 2022) proposed a multimodal fusion method based on text and visual co-attention. IMF (Li et al., 2023) proposed a two-stage interactive multimodal fusion framework for link prediction.

heading: 3 Key Information and Task Definition

heading: 3.1 Chinese Radicals
text: Chinese characters are composed of radicals, which are also characters or variants of character. The number of radicals is less than that of character significantly because they are commonly shared by
different glyphs. Radical can express the semantic or phonetic information, therefore, it is the smallest units to describe Chinese glyphs.

heading: 3.2 Variant Glyphs
text: The variant glyphs (异体字) refer to a group of glyphs with the same meaning, pronunciation, usage but different in their graphics. The representative example is the traditional Chinese character (e.g., “鳥”, bird) and the corresponding simplified Chinese character (e.g.,“鸟”). They belong to the same Chinese character label in this paper.
We divide the variant glyphs based on the following three indicators: (1) different in radical types; (2) different in radical quantity, location or glyph (e.g., “虎”(tiger) and “虍”(tiger)); (3) different in living time stages. We take the Oracle, Bronze and States as three ancient time stages, and others after them as the Modern stage.
In addition, we call a group of variant glyphs as the synchronic variant glyphs if they lived in the same time stage, otherwise, we call them diachronic variant glyphs. Correspondingly, the testing glyphs in this work is divided into two groups: the synchronic testing glyph, if it has at least one synchronic variant glyphs in the training set, otherwise, it is diachronic testing glyph. We will separately show the results of the two groups in evaluation, because the latter is more difficult to be identified and is common in practical applications.

heading: 3.3 ZiNet Knowledge Base
text: The available data for this work is limited, because most of the Chinese character image resources do not integrate at the glyph level. The dataset of this work (Section 6.1) is from ZiNet (Chi et al., 2022). ZiNet is a diachronic knowledge base describing relationships and evolution of Chinese characters. Up to now, it records up to 16000 glyphs in various historical periods of Oracle, Bronze and States, and associates them with the character, radicals and images.

heading: 3.4 Task Definition
text: Our task is shown in Figure 2. In this task, there is a character set C = {ci|i = 1, 2, ..., |C|} containing Chinese character labels; a training glyph set Gtrain = {gi|i = 1, 2, ..., |Gtrain|} containing Chinese glyphs distributed in all of time stages; and a testing glyph set Gtest = {gi|i = 1, 2, ..., |Gtest|} containing Chinese glyph samples in ancient time stages, which is unseen for the
model, Gtrain ∩ Gtest = ∅. Each Chinese glyph belongs to one Chinese character label. Thus, the training dataset is {(g, c)|g ∈ Gtrain, c ∈ C} and the testing dataset is {(g, c)|g ∈ Gtest, c ∈ C}.
The Chinese glyph in this task is described by two kinds of data: the images and a short radical description text (Figure 2). Each glyph gi has one or more than one images, it’s image set is defined as Pgi . And it also has a radical sequence Rgi , which contains the radicals of ranking in the writing order.
There is a graph KG, KG = (V,Rel), V = Vg ∪ Vr, where V is the entity node set, Vg is the glyph entity node set and Vr is the radical entity set. Rel is the edge set between entities. KG contains all glyphs in Gtrain and their radicals, as well as the edges between them.
The goal of this task is to predict the corresponding character label c, c ∈ C for the glyph g, g ∈ Gtest: f(Pg, Rg,KG|θ) = c, where θ is the parameters of the model gotten from trainings. All of the supervised learnings are based on the training dataset and KG.

heading: 4 Chinese Glyph Knowledge Graph
text: As shown in Figure 3, the CGKG has two types of entities of glyph and radical, and two relations: the inclusion relation between glyph and radical Rel1(g, r), and the semantic relation between two radicals Rel2(r, r). Two radicals would be linked with Rel2 if they met the following relationships:
• They belong to the same character (e.g., “虎”(tiger) and “虍”(tiger)).
• They are derived from the same mother character (e.g., “東” (bag) and “束”(tie)).
• They have the indication relationship (指事) (e.g., “生” (to grow) and “屮”(grass)).
• They express the same or similar meaning (e.g., “屮”(grass) and “木”(tree)).
• They have the same or similar pronunciation and usually can be mutual borrowing used (通 假) in the ancient Chinese (e.g., “匕” (female ancestor) and “比”(close to each other)).
• They are the interchangeable radicals (“屮” (grass) and “木” (tree) is a pair of interchangeable radical observed in the variant glyph pair “屮, 屮, 屯, 日” and “木, 木, 屯, 日” of the character “春”).
The first five relations were annotated by our experts, and the last one was automatically extracted from dataset. We automatically extracted interchangeable radical pairs from all of variant glyph pairs in dataset: if only the target radical pair are replaced with each other and other parts are the same, they will be set as the candidate, the radical pairs appeared over 2 times will be added into KG.
These relations are not independent but highly relevant. We do not specify the relationship categories, if two radicals satisfy any of above professional indicators, we will add an edge between them in KG, and finally construct an undirected graph. Ultimately, there are 1907 edges between 657 radical nodes in KG.

heading: 5 Chinese glyph identification Method

heading: 5.1 Method Overview
text: The architecture of MCGI is shown in Figure 4. Given a glyph gk, the inputs include an image set Pgk , a radical token sequence Tgk , which is the textual representation of Rgk and the graph KG.
MCGI consists of three encoder modules: the image encoder (IE) encodes images in Pgk into highdimension embeddings and generates the visual glyph embedding evgk ; the graph encoder (KGE) represents glyph and radical entities in KG as the KG glyph embedding ekggk and the radical embeddings er for all radical entities; the multimodal encoder (ME) generates the multimodal glyph embedding emgk , which fuses the initialized token embeddings of Tgk provided by BERT with the image and radical embeddings from IE and KGE.
Finally, the Decoder module outputs the result based on the glyph representations from three encoders: Decoder(evgk , e kg gk , e m gk ) = {s(gk, ci)|i = 1, 2, ..., |C|}, s(g, c) is the score between the glyph and the character label.
In this section, we will introduce the IE in Section 5.2, the KGE in Section 5.3, the ME in Section 5.4, the Decoder in Section 5.5, and the working steps of MCGI in Section 5.6.

heading: 5.2 Image Encoder
text: For a target glyph gk, IE generates the embedding set E(Pgk) of its images: E(Pgk) = {evigk |evigk ∈ Rdv , i = 1, 2, ..., |Pgk |}, dv is the dimension of the visual feature space. And it’s visual glyph representation evgk is set as the average of E(Pgk):
evgk = 1 |Pgk | ∑
pi∈Pgk
IE(pi) (1)
IE is a computer vision model, such as ResNet. It is pre-trained on the image classification task, which classifies image samples into the character label: fp−>c using the images of the glyphs in training dataset.

heading: 5.3 Knowledge Graph Encoder
text: KGE embeds the glyph and radical entities in KG into E(KGg) = {ekggi |e kg gi
∈ Rdkg , i = 1, 2, ..., |Vg|} for glyphs, and E(KGr) = {eri |eri ∈ Rdkg , i = 1, 2, ..., |Vr|} for radicals, where dkg is the dimension of the graph feature space, Vg and Vr are the glyph and radical node set, respectively, which are the subsets of V .
The KGE is a graph representation model. Here we use node2vec (Grover and Leskovec, 2016) to initialize the entity embeddings and further train a GAT network on the glyph entity classification task fg−>c based on Gtrain.

heading: 5.4 Multimodal Encoder
text: The architecture of ME is shown in Figure 5, which is based on BERT. For each glyph gk, ME inputs each of its image embedding evigk , e vi gk
∈ E(Pgk); the radical token embeddings E(Tgk) initialized from BERT; and the entity embeddings for radicals of gk: E(KGr(gk)). It outputs the fused multimodal representation emigk :
emigk = ME(e vi gk , E(Tgk), E(KGr(gk) )) (2)
We get the multimodal glyph representation emgk by averaging all embeddings in {emigk |emigk ∈ Rdm , i = 1, 2, ..., |Pgk |} gotten from ME, dm is the dimension of the multimodal feature space.
Specifically, for T , we arrange the radicals in order from top to bottom, from left to right and from outside to inside to the maximum extent, and “,” is the spacing symbol. The radical of the single glyph is set as itself. The T should contain all levels of radicals, for instance, T of the glyph g14 (Figure 1) is {艸, 屮, 屮, 屯, 日}, in which “屮, 屮” are the second level radicals making up “艸”.
The specific symbols for BERT are added to T : {[CLS], t2, ..., tn−3, [SEP ], [IMG], [SEP ]},
where n is |T |, [CLS], and [SEP ] are the start and end symbols, [IMG] is the position where will be filled with the image embedding ev, and t2 to tn−3 are the tokens. The initialized embedding sequence of T is: E(T ) = {eti |eti ∈ Rdt , i = 1, 2, ..., n}, dt is the dimension of the textual feature space.
The fusion layer fuses E(T ) with ev and radical embeddings from E(KGr), and generates an unified input embedding sequence E(O), E(O) = {eoi |eoi ∈ Rdt , i = 1, 2, ..., n}:
   eoi = Linear(ev), i = n− 1 eoi = Linear(eti ||er(ti)), r(ti) ∈ V ′ r
eoi = eti , Otherwise
where Linear is the linear layer and || is concatenation operation, r(ti) is the radical entity corresponding to the token ti and er(ti) is the radical embedding of it. V ′r is the set of radical entities that have Rel2 relation in KG, V ′r ⊂ Vr. E(O) is fed to ME and outputs the hidden visual representation hv of on−1, and the hidden textual representation ht, which is the average of the outputs of o1 to on−2, at the last layer. The multimodal embedding em is:
em = hv + ht (3)
We pre-train ME on the classification task based on the training data. The em will be mapped to the scores for character labels through a linear layer and softmax function.

heading: 5.5 Decoder
text: We take a unsupervised Decoder. Every character label ci corresponds to a set of glyphs G(ci) = {gcij |gcij ∈ Gtrain, j = 1, 2, ...,m}, m is the number of glyph of ci in Gtrain. We calculate the Cosine Similarity between each glyph embedding pair
in Gtest and Gtrain and set the score of (gk, ci) as the max score between gk and the glyphs in G(ci).
There are three similarity scores between gk and ci: sm(gk, ci), sv(gk, ci) and skg(gk, ci), corresponding to the three input glyph embeddings of emgk , e v gk , and ekggk . The final score s(gk, ci) is:
s = αsm + βsv + γskg (4)
where α, β and γ are hyperparameters. For comparison, we also realized a supervised Decoder which is trained on glyph classification: fg−>c, in which the hidden glyph representation is egk = αLinear(e m gk )+βLinear(evgk)+ γLinear(ekggk ), where α, β and γ are parameters gotten from training.

heading: 5.6 Working Steps and Loss Function
text: MCGI follows a staged training and predicting framework: in the pre-training stage, it separately trains encoders of IE, KGE and ME, and acquires the visual, graph and multimodal glyph embeddings for glyphs in Gtrain, as well as the radical entity embeddings.
In the predicting stage, given a glyph gtest: (1) it acquires image embeddings and visual glyph embedding through IE; (2) adds gtest into the KG, including creating a new glyph node and linking it to the corresponding radical nodes, and trains KGE to get the KG glyph embeddings for gtest and glyphs in Gtrain; (3) acquires the multimodal embedding through ME; (4) outputs the character label through Decoder.
We use the cross-entropy loss on the training of three encoders. Given a (x, ci) pair, x is the target glyph or image, s(x, ci) is the score between x and the character ci, the loss function is:
loss = −s(x, ci) + log |C|∑
l=1
exp(s(x, cl)) (5)

heading: 6 Evaluation

heading: 6.1 Dataset Information
text: The statistics is shown in Table 1. It has 6941 character labels and 1974 radicals. There are 14931 glyphs and 53452 images for training, and 1279 ancient glyphs and 5319 images for testing. The glyph samples are distributed in different time stages (Oracle, Bronze, States and Modern).
There is an average of only 2.15 (14931/6941) glyph samples per character label for training. It
is because of the incompleteness of the current dataset in this field, and the real-world discovered variant glyphs themselves are limited in quantity. Thus, in the current research, the testing glyphs only overs 805 commonly used character labels, and each testing glyph has at least one variant glyph in the training set.
And the proportion distributions of image and radical quantities for training glyph samples are shown in Figure 6. The average number of image for per glyph is 3.73 and radical is 2.62. The image dataset has the long tail effect due to the differences in usage frequency. It conforms to the real world distribution for ancient Chinese character.
The categories of testing glyphs is shown in Table 2, among the 1279 testing glyphs, there are
598 synchronic testing glyphs (S, Section 3.2) and 681 diachronic ones (D). In addition, we count the consistency of radical types (Section 3.2) between testing glyphs and their variant glyphs that are in training set. The testing glyph in “Consistent” group has at least one training variant glyph with the same radical types as it (they are only different in radical location, quantity, glyph or time stage), while the testing glyph in “Inconsistent” group changed the radical types. There are 1024 inconsistent testing glyphs and only 255 consistent ones, which makes a certain difficulty for this task.

heading: 6.2 Indicators and Baseline Models
text: Our evaluation indicators include: R@1, R@3 and R@10, which are the average proportion of topn correct label rankings; the Mean Rank (MR) and the Mean Reciprocal Ranking (MRR). The potential application scenario of this work is to recommend experts with a set of most possible (top-n) character labels to inspire their thoughts and reduce the candidate character scope. So the ranking score is most suitable to evaluate this task and the MR score can show the human workload in the real applications.
We classified the models according to the modality of data they used. The uni-modal models includes ((v) is visual modality; (t) is textual modal-
ity; (k) is graph modality):
• (v): (1) DesNet121; (2) ResNet50 (ours): we pre-trained a ResNet model on the image dataset of ancient Chinese characters based on unsupervised contrastive learning.
• (k): GAT_CGKG, the GAT network on CGKG.
• (t): (1) LCS (longest common subsequence) of the radical text; (2) SikuBERT1, which was trained on ancient Chinese corpus; (3) BERT (ours): we trained it from SikuBERT on the unearthed ancient Chinese contexts.
All the uni-modal models were trained on the glyph or image classification tasks. For the (v) models, the score between (g, c) is set to the sum of the (p, c) scores of all images of the glyph g.
There are four multimodal baseline methods (Section 2): (t+v) ViLT (Kim et al., 2021); (t+v) HRGAT (Wang et al., 2022); (t+v+k) IMF (Li et al., 2023); and (t+v+k) GlyphSim (Chi et al., 2022). Apart from GlyphSim, their tasks are very different from ours. We only realized their multimodal
1https://huggingface.co/SIKU-BERT/ sikubert
fusion parts and trained their models on glyph classification task, to show the effects of the potential available methods on our dataset and task.
For our MCGI, the IE, KGE and ME encoders used the uni-modal models of ResNet50 (ours), GAT_CGKG and BERT(ours) respectively. To do the ablation study, we firstly individually evaluated the three kinds of ME encoder that use different combinations of T , KGr and P information: ME(t+v+k); ME(t+v); and ME(t+k), in which ME(t+v+k) is our full method. And then, we evaluated our full MCGI method: ME(t+v+k)+KGE+IE in Decoder and several combinations of three encoders: ME(t+v+k)+IE; ME(t+v+k)+KGE; ME(t)+KGE+IE; ME(t+v)+KGE+IE. We also evaluated the supervised Decoder for comparison: ME(t+v+k)+KGE+IE-(s).

heading: 6.3 Configuration
text: In pre-training of IE, the batch size was 64, the learning rate was 0.001, epoch was 150 and dv was 768; For ME pre-training, we used the basic BERT with 768 dimensions, batch size was 64, the learning rate was 0.00002, dm was 768, and we trained it for 100 epochs. For KGE, the embeddings were initialized by using the opened OpenNE2 tool, we used their default parameters and the dimension of the output vector was 1000. And we trained GAT network through 5 epochs, the learning rate was 0.0005 and batch size was 256, the dk was 2000. The hyperparameters α, β and γ in Decoder were 0.4,0,5, and 0,7 respectively, which were set by testing 50 extra validate glyph samples on the combinations between 0.1 and 1.0. For all supervised models, we ran for 3 times and chose the best score as the result. The training time of our ME and IE is about 8 hours on the NVIDIA RTX 3090 GPU.

heading: 6.4 Results and Discussions
text: Validity of our method: The results are shown in Table 3, our model achieves the best results in most indicators except MR compared with the baselines.
The results of ME encoder (ME(t+v+k)), on the D group, are improved from the uni-modal and also better than the multimodal data fusion baselines (Kim et al., 2021; Li et al., 2023; Wang et al., 2022). However, we observed slight declines for S group after fusing the visual features (comparing BERT(ours) and ME(t+v)).
2https://github.com/hengdos/OpenNE
The Decoder module further enhances the overall performance. The ablation study proves the validity of all encoders: (1) it shows the contribution of our KGE encoder and the graph CGKG (comparing ME(t+v+k)+IE and ME(t+v+k)+KGE+IE; ME(t+v+k) and ME(t+v+k)+KGE). It improves the top-n rankings especially for S group, and reduces the MR indicator observably; (2) by comparing directly using BERT (ME(t)+KGE+IE) and using ME encoder (ME(t+v+k)+KGE+IE)), it proves the effectiveness of our ME encoder, especially for improving the results for D group; (3) we did not observe the improvement of supervised Decoder (ME(t+v+k)+KGE+IE-(s)) in most indicators. It may because that one character label only has limited glyph samples for training in our specific task; (4) the unsupervised method GlyphSim (Chi et al., 2022) has better MR scores compared with ours, however, we have much better top-n rankings through pre-training of encoders.
Effectiveness for each modality: There are overlarge pictorial differences between ancient variant glyphs, which limited the performance of (v) methods. The results of (t) and (k) models are better than the visual models, the (t) models have better scores in the top-n ranks and (k) model suits to reduce the mean rankings. However, we also observed limitations of them, most glyphs contain less than 3 radicals, which is insufficient to distinguish features of similar characters. We randomly selected 150 error samples for ME(t+k) model, and found that 108 of them were classified into character labels with very similar radical combinations. The multimodal fusion can leverage the advantages of each modality, in which the radical semantics has a better effect on the S testing group (ME(t+k)), while the D group more requires visual features (ME(t+v)). The reason we analyzed is that there is a greater radical semantic correlation between S variant glyphs, making them benefit more from our glyph knowledge graph.

heading: 7 Limitations
text: The limitations of this work includes: (1) We only tested 1279 deciphered glyphs in 805 commonly used character labels. The uncommon or undeciphered glyphs should be more difficult to be reasoned, thus the effectiveness would be reduced. And the datasets in this field are imperfect currently for those uncommon characters.
(2) Our method only suits for the glyph who has variant glyphs in training set. And the effects of the method were observed slightly worse for those glyphs with single radical, as well as the glyphs whose radicals are both newfound and not shared with training glyphs, but this situation is rare in this task.
(3) Except the radical and visual features, the contexts of historical documents, dictionary records, pronunciations and so on are very important, which should be considered in the future.
Currently, we are associating Chinese glyphs with more textual resources such as the historical documents, dictionary records and so on. The ultimate goal is to provide a complete methodology and refined model for ancient Chinese character deciphering. And in application level, we will develop the glyph recognization and recommendation serves based on this work.

heading: 8 Conclusion
text: This paper introduces an innovative research of glyph identification of ancient Chinese characters and devises a knowledge powered multimodal method based on visual and radical semantic information for this task. The results proved the effectiveness of our glyph knowledge graph and the multimodal method, which achieves the best results in most indicators, and shows the potentials of visual and radical information in this task for the first time. This work can be applied in the related fields of ancient Chinese linguistics and characters.

heading: Acknowledgements
text: This article is a phased achievement of the National Social Science Foundation project “Research on Chinese Pre Qin Language and Culture Based on Jin-wen Data”(number:23VRC033), and has been supported by the Youth Project of the Philosophy and Social Science Research Innovation Team of Jilin University “Inheritance of Chinese Character Culture and New Forms of Human Civilization”(number:2023QNTD02).

heading: Future Work
text: (3) Except the radical and visual features, the contexts of historical documents, dictionary records, pronunciations and so on are very important, which should be considered in the future. Currently, we are associating Chinese glyphs with more textual resources such as the historical documents, dictionary records and so on. The ultimate goal is to provide a complete methodology and refined model for ancient Chinese character deciphering. And in application level, we will develop the glyph recognization and recommendation serves based on this work