UniCalli: A Unified Diffusion Framework for Column-Level Generation and Recognition of Chinese Calligraphy
Authors: Tianshuo Xu
Kai Wang
ZhiFei Chen
Leyi Wu
Tianshui Wen
Fei Chao
Ying-Cong Chen
Venue: ICLR 2026 Poster (2026)
Keywords: calligraphy
Primary area: generative models

Abstract: Computational replication of Chinese calligraphy, a cornerstone of cultural heritage, remains challenging. Existing methods split into two flawed camps: some render high-quality isolated characters yet miss page-level aesthetics (ligatures, spacing, scale), while others attempt page/column synthesis but sacrifice calligraphic correctness. We introduce UniCalli, a unified diffusion framework for column-level recognition and generation. Training both tasks in one model is deliberate: recognition constrains the generator to preserve character identity and stroke structure, while generation supplies strong style/layout priors—together fostering concept-level abstractions (radicals, stroke configurations) that improve both tasks under long-tail, limited-label regimes. We curate a dataset of 8,000+ digitized pieces, with ~4,000 densely annotated (script labels, character boxes, transcriptions). UniCalli employs asymmetric noising and a rasterized box map to inject spatial priors, and is trained on a mix of synthetic, labeled, and unlabeled data. The model is robust to rare styles, better disentangles style from script, and attains state-of-the-art generative quality with clear gains in ligature continuity and layout fidelity, alongside stronger recognition. The framework extends to other ancient scripts, demonstrated by successful transfer to Oracle bone inscriptions and Egyptian hieroglyphs. Code and data will be released.

Published as a conference paper at ICLR 2026
UNICALLI: A UNIFIED DIFFUSION FRAMEWORK FOR COLUMN-LEVEL GENERATION AND RECOGNITION OF CHINESE CALLIGRAPHY
Tianshuo Xu', Kai Wang', Zhifei Chen', Leyi Wu', Tianshui Wen', Fei Chao?, Ying-Cong Chen 14 'HKUST(GZ), CCina University of Geoscience Beijing, 'Xiamen University, "HKUST txu 70..447ccnnect... www..thtttttogooo edu.cnd en; pingcongchen@ust.
("Corresponding Author)
Calligrapher (Script)
1 Hual Su (Cursive)
Nal (fT) Tang Vin (Running)
R:T (#f) Yan Zhenqing (Regulas)
KCKii (*)) Ouyang Xun (Rogular)
|
1
1
*
1575 (fF)
RA (H))
$::1- (#A)
T:4k::: (#t)
ML Fu (Running)
Zhao J1 (Regular))
Zhang Fithi (Regular)
Wang Xishi (Cursiva)
"Bring the Wine" Li Bai
Do you not see the Yellow River's waters pouring from the heavens? They surge into the sea, Do you not see in the bright mirrors of high halls grief for white bair? dawn, like black silk; by dusk, has
return.
AL. 2
7 SL
A 1
snow.
Figure I: Generated by the UniCalli model, this image displays calligraphy from Li Bai's poem "Bring in the Wine". Each column showcases different master's style to demonstrate the model's versatility. Notably, especially in the Cursive script, the model generates contextually appropriate connecting strokes and character sizes based on adjacent characters. An English translation and the original Chinese text are provided in the lower corners. The complete works by each calligra- pher are available in the Appendix (The calligraphic background has been manually edited for presentation.)
ABSTRACT
Computational replication of Chinese calligraphy remains challenging. Exist- ing methods falter, either creating high-quality isolated characters while ignoring column-level aesthetics like ligatures and spacing, or attempting column synthesis at the expense of calligraphic correctness. We introduce UniCalli, unified diffu- sion framework for column-level recognition and generation. Training both tasks jointly is deliberate: recognition constrains the generator to preserve character structure, while generation provides style and layout priors. This synergy fos- ters concept-level abstractions that improve both tasks, especially in limited-data regimes. We curated dataset of over 8,000 digitized pieces, with 4,000 densely annotated. UniCalli employs asymmetric noising and a rasterized box map for spatial priors, trained on mix of synthetic, labeled, and unlabeled data. The model achieves state-of-the-an generative quality with superior ligature continuity and layout fidelity, alongside stronger recognition. The framework successfully extends to other ancient scripts: Oracle bone scripts and Egyptian hieroglyphs.

---

Published as a conference paper at ICLR 2026
INTRODUCTION
Chinese calligraphy is a central vehicle of Chinese culture and a world heritage art form, practiced and studied by millions (Yangbol 2022, Nihon Shuji 2025). While recent deep learning has pro-
duced recognition (Luo et al. 2025; /hhou et al.) 2025) and generative (Liao et al.] 2023] [202]] Wu et al. 2020) systems, progress is hindered by scarce data and long-tail distribution of styles. Consequently, existing generative methods are limited. On one hand, isolated-characte synthesis
and font transfer techniques (Zhang al. 2024 Yang et al. 2024 Xie al.. 2021) can produce high-quality individual characters but ignore the holistic aesthetics of a finished work: column-level composition, spatial rhythm, and the crucial inter-character ligatures that convey artistic in- tent. On the other hand, general image generation models (Labs et al. [2025] Esser et al. 2024) and VL.M-based systems (Bai al. 2025| OpenAT, 2024) Gong et al. (025) that attempt column-level synthesis often fail on correctness, rendering characters with improper forms or styles. This leaves a critical gap for generating complete calligraphic works that are both structurally sound and artistically coherent.
To address these challenges, we contribute both a dataset and model. Dataset. We curate a corpus of more than 8,000 digitized works spanning 93 classical calligraphers (e.g.. Wang Xizhi, Mi Fu, Ouyang Xun). Over 4,000 works works-covering hundreds thousands of characters- are annotated with script type (regular/kai, running/xing, cursive/cao), per-character bounding boxes, and modern- character transcriptions. Method. We introduce UniCalli, a unified diffusion-based framework that learns jointly from synthetic, labeled, and unlabeled data, improving robustness in long-tail and limited-label regimes.
Unlike pipelines that separate recognition and generation, UniCalli integrates them in single model with shared representations. This coupling is intentional: the recognition objective pressures the generator preserve character identity and legibility, while the generative objective supplies strong style/layout priors and rich augmentations that make recognition more reliable across writers and scripts. The joint training encourages the model to form transferable, concept-level abstractions of characters (radicals, strokes, structures) that benefit both tasks and reduce label dependence. Con- ditioned on text, calligrapher identity, and script, UniCalli composes vertical, column-wise layouts with inter-character ligatures and deliberate control over character scale and spacing, producing complete-we work outputs rather than isolated glyphs while maintaining stylistic consistency and char- acter accuracy.
Architecturally, UniCalli builds on a Multimodal Diffusion Transformer (MMDiT) backbone (Blatmann et al. 2023), departing from causal autoregressive rollouts. During synthesis, the dif- fusion transformer attends bidirectionally over the full canvas at each step, enabling globally con- sistent layout decisions that mirror how calligraphers plan a page before committing strokes. To unily recognition and generation, we apply asymmetric noising to two coupled latents-a clean "standard-font" rendering of the target text and calligraphy image. Noising the calligraphy branch while keeping the Handard-font branch clean yields generation; reversing this configuration yields recognition. To strengthen spatial reasoning (character extents, column alignment, inter-character spacing), we augment the calligraphy input with a rasterized bounding-box map and jointly denoise the pair under shared schedule, helping the model internalize position and scale priors that improve ligature formation and column rhythm.
Empirically, UniCalli performs strongly on both tasks. On recognition benchmarks, the unified model attains accuracy comparable to task-specialized recognizers. For generations, quantitative metrics and human evaluations indicate state-of-the-an results in glyph correctness and stylistic fidelity. Beyond Chinese calligraphy, further validate the framework on Oracle bone inscriptions and Egyptian hieroglyphs, demonstrating adaptability across writing systems and highlighting its potential for the digitization and study of ancient scripts.
Our main contributions are threefold:
A Large-Scale Annotated Calligraphy Dataset: We present a new, large-scale corpus of over 8,000 classical Chinese calligraphy works. More than 4,000 of these are annotated with script type, per-character bounding boxes, and modern transcriptions, providing a valuable resource spur research in column-level analysis and generation.
2

---

Published as a conference paper at ICLR 2026
UniCalli, a Unified Recognition and Generation Framework: We propose novel diffu- sion transformer model that, for the first time, unifies column-level calligraphy generation and recognition. Its bidirectional attention mechanism enables globally coherent composi- tion, moving beyond isolated characters to produce complete, stylistically consistent works.
Demonstrated State-of-the-A Performance and Generalizability: We show that Uni- Calli achieves state-of of-the--rt results in generative fidelity and competitive performance in recognition. Furthermore, we validate its adaptahility on other complex and ancient writing systems, including Oracle bone script and Egyptian hieroglyphs, demonstrating the broad potential our approach.
RELATED WORK
2.1 DIFFUSION MODELS
Diffusion models (Ho et al. 2020 Song et al. 2020 Lipman et al., 2022) have recently emerged as a powerful class of generative models, achieving sfate-of-the-an results in high-fidelity image synthesis. Their core mechanism consists of two processes: a fixed forward process that gradually adds Gaussian noise to an input image until it becomes pure noise, and learned reverse process. In the reverse process, neural network, typically a U-Net (Rombach et al. (0222) or a Transformer (Peebles Xie 2023 Bao et al. 2023a), trained iteratively denoise a random input, step-by- step, generate a clean sample.
The Multimodal Diffusion Transformer (MMDiT) Blattmann et al. 2023) departs from causal, au- toregressive models (Esser et al. 2021 Yu et al. 2022) by using bidirectional attention, making ideal for tasks requiring global compositional planning. This principle of modular control underpins flexible frameworks like Unidiffuser (Bao al. (023b) and Uni-renderer (Chen al.] (202).. By assigning independent noising and denoising schedules to each modality, these models enable a ver- satile "any-to-any" generation paradigm, where any subset of modalities can condition the synthesis of the rest.
2.2
CHINESE CALLIGRAPHY GENERATION
Early studies in Chinese calligraphy generation, whether based on GANs (Wu
2020 Xie
et al. 2021 Tang & Lian 2021) or high-fidelit diffusion models (Zhang et al. [2024] Liu & Lian
He al., [2244] Dai et al. 2024), have primarily treated the task as one-shot or few-shot font generation. These methods excel at synthesizing isolated characters with accurate structure (Zeng ctt al.
2024
2023) but do not address page-level composition.
More recent efforts have shifted focus to page-level compositional synthesis, but these approaches exhibit critical limitations. On one hand, seneral-purposs image generation models and VLM-baased systems can render full compositions but often fail on correctness, producing characters with im- proper forms or styles (Bai et al.) 2025 OpenAl, 2024; Gong et al., 2025). On the other hand, specialized models like CalliPaint (Liao et 2023), Moyun (Liu et al. 2024). and Calliffusion (Liao et al. 2024) generate characters sequentially. This autoregressive or sequential nature prevents holistic planning, leading deficiencies in the global layout, rhythm, and inter-character ligatures that define a finished piece. In contrast, our work employs a non-autoregressive framework that plans the entire layout jointly, enabling globally coherent composition that is both stylistically consistent and structurally accurate.
2.3 CHINESE CALLIGRAPHY RECOGNITION
The field of calligraphy recognition has evolved from analyzing isolated characters to employing end-to-end sequence-to-sequenee models that handle connected and cursive scripts. A classic deep learning paradigm is the Convolutional Recurrent Neural Network (CRNN) (Shi et al. 20166), which combines CNN feature extractor with an RNN decoder. More recently. the domain has been domi- nated by more powerful Transformer- based architectures (Dosovitskiy et al. (2021). D. State-of-the-an art models, such as OracleNet (Zhou et al. 2025 and CalliReader (Luo et al. (025). leverage these modern backbones to achieve high accuracy on stylistically diverse and irregular layouts. However, these highly effective methods task-specialized recognizers. They operate independently of the
3

---

Published as a conference paper at ICLR 2026
Image
Image
Image
(a) Two Timestops Noising Schedule
Generation: f, = 0, rand(():
Random [ A]
noise:
Recognation: 1 rand(
Tokens
(b) RoPF Duplication with Vodulate Embedding
Calculate
RoPF
Ve R
0
0
2
() lesenabll
Duplicate
embhctkliggs
DualT MMDIT Block
(c) Dual Timesteps Multimodal DiT Block
Ue
30.17
scalee
scale
shif.
shifl
53
Pred. Images
30
Tokens
Input
Self Arta
Gating
Figure 2: An overview of the UniCalli framework. Abbreviations follows: Cond. (Condition), Pred. (Prediction), Attn. (Attention), and RoPE (Rotary Positional Embedding).
generative process and lack shared representation, critical gap our unified framework directly addresses by integrating both tasks.
METHOD
This section details the methodology of our proposed framework, UniCalli, with an overview of the complete pipeline provided in Figure 2 We begin in Section [3. introducing the core principle of our approach: unified framework that treats calligraphy generation and recognition mutually enhancing dual tasks. Following this, Section [32 delves into the specific architectural innovations for column-level modeling, including the use of a box map latent and Duplicate RoPE strategy to fuse spatial information. Subsequently, Section 3.3 presents our Conditional Dropout technique, designed disentangle style and glyph information and mitigate style overfitting. Finally, Sec- tion 3.4 describes our joint training scheme, which leverages combination of labeled, unlabeled, and synthetic data to enhance the model's overall generalization capabilities.
3.1 UNIFIED FRAMEWORK FOR BIDIRECTIONAL LEARNING
We begin by defining our notation. Let the content image (a standard-font rendering). the calligraphy image, and its corresponding bounding box map be denoted by Le R33HXW 1r x and Im E x respectively. These images are first projected into latent space using pre- trained Variational Autoencoder (VAE) encoder, denoted as E. This yields the latent representations =e E(T), 2, E(T,), and 2.. {. We employ two independent timesteps, te, t, E [0. 1), where controls the noising process for the content latent and &ę governs the noising applied jointly to the calligraphy latent z, and the box map latent km.
At the heart of UniCalli is the principle that calligraphy generation and recognition are dual tasks that can mutually enhance one another. model proficient in generating a character's visual form from its abstract identity should inherently possess the features needed to recognize that character from its image, and vers.. By training these two capabilities within a single, unified model, we enable the sharing of representations, forcing the model to learn more robust and holistic understanding of the relationship between text, style, and spatial layout. This synergy is the core motivation for unified design.
Our framework actualizes this principle through a training procedure that operates in one of two modes: generation or recognition, selected randomly each training step. The entire process is
4

---

Published as a conference paper at ICLR 2026
built upon the latent representations 2e, 24, and 2m- We corrupt these latents using the flow- fow-matching technique (Lipman et al. 2022). For a given latent 2 and timestep t4 E [0, 1]. the noised latent is 26 t. Eg + tg)== ) 2kk where N (0, I). The training mode dictates the timestep assignments. For generation, we aim to create an image and layout from content, so we set the content timestep le 0 (no noise, used condition) and sample the image timestep i, uniformly from [0, 1]. For recognition, the goal is to infer content from an image, so we set t; 0 and sample & from [0. 1].
This dual-mode approach is mirrored in our composite loss function. Let Loood Ling, and Loox be the standard flow-matching losses for the latents. The total loss Loral is conditioned on the training mode. In generation mode, the objective is to reconstruct the image and box map:
Lioill Limg + Loox A Leond-
Conversely, in recognition mode, the objective is to reconstruct the content:
Tooall Lond
(Timg + Ttbox)
Here, À is balancing hyperparameter empirically set to 0.02. This dual-objective strategy steers the
unified model to learn the complete bidirectional mapping between content and its visual rendering.
3.2 ARCHITECTURE FOR DOLUMN-LEVEL MODELING
While the unified framework provides the learning structure, achieving high-fidelity column-level calligraphy requires specific architectural designs that can master the intricate spatial relationships between characters. We introduce two key innovations to address this.
First, to provide the model with explicit guidance on spatial layout, we incorporate a box map latent (2m). This latent representation encodes the precise bounding box (position and scale) of each character within the column. By tasking the model with predicting this map during generation, we force it to learn the core principles of calligraphic composition. This explicit supervision of the column's structure is fundamental for rendering complex details accurately, such as realistic character spacing, size variations, and natural-looking ligatures that connect adjacent characters.
Second, for the model to effectively utilize the box map, the spatial information across all three modalities-contee (ze), image (z4), and box map (zm) (zm)-must be seamlessly fused. To achieve this, we propose Duplicate RoPE with Modulated Embedding strategy. This technique estab- lishes a shared spatial coordinate system for all modalities. We first compute the Rotary Position Embedding (RoPE) (Su et al. 2024) for the calligraphy image latent 2;. This RoPE, which tains rich 2D positional information, is then replicated and applied to both the content and box map latents. To allow each modality to retain its unique identity within this shared system, add distinct, Iearnable modulation embedding (Emod) to each copy:
RoPE, = CalculateRoPF(zz)).
RoPE, RoPE; + EmoLk for k E {c.m).
This shared-yet-distinct repressnattnn is the key that enables the model to build strong bidirectional associations, allowing it to, for example, determine character's appearance based on its identity and position, or infer its identity based on its appearance and position.
Finally, these spatially-aware latents are processed using an adapted MMDIT (Blattmann et al. 2023) block. The input tokens for each modality are modulated by their respective timesteps (T. = to T Tm t.) before being concatenated and passed through a shared self-attention layer. This allows the model to integrate information from all modalities, conditioned on their individual states in the diffusion process, within a single, powerful block.
3.3 DISENTANGLING STYLE AND GLYPH VIA CONDITIONAL DROPOUT
Standard conditional training often leads to overfitting on long-tail stylistic data, where the model prioritizes rare style constraints at the expense of correct glyph structure. To mitigate this, we employ strategy to disentangle style representation from glyph formation via conditional dropout. Specifically, we mask the content condition with pure noise at a probability paoop by setting the condition timestep f. to 1:
te { { with probability parop
(3)
with probability - Pdrop
5

---

Published as a conference paper at ICLR 2026
Conditio
Ref.
UniCatii
ChaioPTs
Emiek.1
Deaboo FovtDifleser
Condition
Ref.
UniCaall CharGert
Emick.
Footbeilterr
1 Pレード'アE
マフストレ
x
23
#
ST (Rogstar)
Correctress:
Style Conrectiess:
Conditon
Rdf.
FootDuffiser
Chataris
H
TSA
1
2
1
i
K
P
i
HUIN 1,03
Suxt Causting (Cumiie))
Wang Xiata (C (canii))
Style Correctness:
Style Conrectness:
Figure 3: Qualitative comparison of our model against ofate-of-the-arr generative models. To fa- cilitate analysis, we use the following visual annotations: red cross X marks incorrectly rendered glyphs, while red circle O indicates omitted characters. Furthermore, we highlight desirable calli- graphic features: red boxes D showcase well-formed connecting strokes, and blue boxes Q empha- size appropriate, context-aware character sizing. Beneath each generated image, we provide style correctness to evaluate its stylistic fidelity.
Empirically, a dropout rate of Päoo = 0.05 proves most effective. We observe that excessive dropout yields generic, style-agnostic outputs, while insufficient dropout leads style over-prioritization, resulting in abstract patterns with incoherent glyph structures.
3.4 JOINT TRAINING ON LABELED, UNLABELED, AND SYNTHETIC DATA
The strategy of stochastically replacing the condition with noise, detailed in the preceding section, can be framed as a form of unconditional generation. This perspective offers a natural mechanism for incorporating unlabeled data into our training paradigm. Specifically, data samples lacking an- notations are processed by setting the condition timestep t. 1, which forces the conditional latent into a pure noise state, こ = ec. This technique seamlessly integrates unlabeled data by treating its generation as an unconditional task, thereby enriching the model's 's understanding of diverse calli- graphic styles.
Furthermore, to expand the model's glyph repertoire and improve its structural understanding of characters, we incorporate large corpus of synthetic data. We curated a collection of TrueType Font (TTF) files for various script styles, including Regular/Kai, Running/Xing, and Cursive/Cao. These fonts were employed to render extensive content from both ancient and modern Chinese literature. The joint training on synthetic data significantly broadens the model's known character set, while the unlabeled data enhances its grasp of calligraphic styles. Collectively, these heterogeneous data sources substantially improve the model's overall generalization capabilittes.

---

Published as a conference paper at ICLR 2026
4 EXPERIMENTATION
In this section, we detail our experimental setup, including the dataset and model architecture. We then present comprehensive evaluation of our model, UniCalli. In Section 4.1, we benchmark UniCalli's generation and recognition capabilities against several state-of-the-et models. To vali- date our design choices, we conduct a series of ablation studies in Section 4.2 Finally, to assess the model's robustness and generalizability, we extend our evaluation to the challenging domains of ancient scripts in Section 4.3
Dataset. Our experimental dataset was constructed from multiple sources. We first curated a col- lection of over 8,000 Chinese calligraphy images (examples can be viewed at Appendix [). fea- turing the works of 93 calligraphers across five major script styles: Regular/Kai, Running/Xing, Cursive/Cao, Clerical/Li, and Seal/Zhuan. From this collection, subset of over 4,000 images was annotated, yielding more than 150,000 character instances, each with specified bounding box and character label.
59
33
3F
#
:111
F
4
31
1
# 18888
Comtition
Conctng d Cennerss
Steld Consono
(R))
EL
SE (90)
Rabl Ohe ler
to Beck O9t)
Chaggi
WORL ehg
As (IF)
AL (Ev) Thae Merade
Br dit
Chomming!
SEB Cuff pescrrppiin
Figure 4: Demonstration of UniCalli's robust multi-style calligraphy generation. The same content rendered in the distinct styles of various calligraphers. The top panel showcases the generation of two Chinese celebrity names, while the bottom panel displays a birthday greeting. This versatility highlights UniCalli's robustness capturing diverse calligraphic styles and its strong potential for wide range of downstream applications.
Model. We fine-tune the FLUX (Labs et al. 2025) backbone with our Duplicate RoPE with Mod- ulated Embedding strategy. The model's input consists of three images: a standard content image, a calligraphy style reference, and a bounding box map. For each sample, we crop region of five consecutive characters and resize both the crop and its box map to 128 x 640. The content image is formed by horizontally concatenating five 128 x 128 standard-font renderings. All three 128 x 640 images are then patchified and concatenated before being input to the model. We fine-tune for 500k iterations on 8x 5xH100 GPUs.
4.1 COMPARISONS
Generation. We benchmark UniCalli's generation capabilities against one-shot font generation method: FontDiffuser (Yang et al. 2024), VI.M-based models: ChatGPT-5 (OpenAl 2024). Emie-
7

---

Published as a conference paper at ICLR 2026
4.5-Turbo (Research 2025). and Doubao (Gong al. 2025) in two settings. For reference-based synthesis, where a ground image exists, visual comparisons are shown Figure 3 and quanti- tative metrics (L1, SSIM, LPIPS, FID) reported in Table For reference-free synthesis, Figure demonstrates UniCalli's ability to generate a character in diverse styles given the same content. Fi- nally, we conducted user study (details in Appendix[H] HH) with 20 calligraphy enthusiasts who ranked the outputs based on Style Consistency, Glyph Accuracy, Naturalness, and Overall Preference, with results summarized in T
Table I: Unified user study results for reference- free calligraphy generation. We report the (mean, standard deviation) of user ratings on a 5-point Likert scale (1=Worst, 5=Best) across four key metrics. The metrics are: Style Fidelity, Glyph Accuracy, Naturalness, and Overall Preference. The best score in each category is highlighted bold. The arrow (t) indicates that higher scores are better.
Method
Style Fidelity!
Glyph Accuracyt
Naturalness?
Overall Preference!
FontDiffuser
1.680, 1.420
4,950, 0,380
2.120. 1.550
1.890, 1.380
ChatGPT55
2.987, 1.205
3.853, 1.163
2.373, 1.334
2.373, 1.220
Ernic-4.5
2,000, 1.166
3,560, 1.369
2,533, 1.226
2,507, 1.204
Doubao
2.413, 1.234
4.800, 0.462
3.520,
3,933, 0.573
UniCalli
4.267, 0.943
4.827, 0.443
4.520, 0.755
4.560, 0.787
Table 2: Character-level recognition accuracy the held-out test set; bold indicates the best per row. "Doubao-1.55 "Ernie-4.5** and "Qwen-2.55** denote Roubao-1.5-Thinking ---Thnniin--vssion-PProo Ernie-
4.5-Turbo-VL, and Qwen-2.5-VL-77B respectively.
Category
GPT-40
Ermie-4.5*
Qwen-2,5*
GOT-OCR2.0
PP-OCRv5
Doubso-1.5
CalliRender
Ours
Cursive/Cuo
0.073
0,255
0.127
0.091
0.091
0,200
0.164
0.109
RegularKKai
0,502
0.616
0.600
0.314
0.396
0,588
0.600
0.688
Clerica/LLi
0.293
0.453
0.453
0.187
0.160
0.507
0.507
0,518
Running/Xing
0.364
0,600
0.473
0.336
0.436
0.545
0.658
0.528
Seal/Zhuam
0.067
0.133
0.067
0.000
0.000
0.133
0.067
0.050
Total
0.380
0.534
0.482
0.266
0.324
0.510
0.533
0.540
Recognition, We benchmark our recognizer against six models, including GPT-4o (OpenAl 2024)
and others. From the labeled data, we created 100-image validation and test sets. All models were evaluated on this common test set, and model was selected based on validation performance. As shown in Table 2 our method attains the highest overall character- accuracy across five script styles. We acknowledge that this advantage may be partly due to the similar distributions of training and test data.
4.2 ABLATION STUDIES
Components ablation. We systematically evaluate the impact of the three main components of our framework (Table 4 (1) the Duplicate RoPE with Modulated Embedding. (2) the Joint Training Strategy utilizing synthetic, labeled, and unlabeled data, and (3) the Conditional Dropout mecha- nism. To establish rigorous comparison, we define the Baseline as standard FLUX Labs et al. (025) model trained exclusively on the generation task. In this baseline setup, the clean condi- tion, noisy image, and mask are concatenated into single input sequence processed by a single, continuous RoPE, without our proposed structural decoupling or joint optimization.
Value of Parop- The Parop (Eq. 3 value the primary hyperparameter in our Conditional Dropout mechanism. It influences the model's focus, creating trade-off between learning the fundamental glyph structure and capturing the specific calligraphic style. A higher Purop value encourages the model to ignore stylistic information and concentrate the core character shape, while lower value allows for more stylistic detail to be preserved, as shown in Figure
4.3 ROBUSTNESS ON ANCIENT CHARACTERS.
Oracle bone script. We trained our model on the HUST-OBC dataset (Wang et al.) 2024) and structured evaluation into three distinct tasks. First, we assessed the model's ability generate
8

---

Published as a conference paper at ICLR 2026
Ref.
Table Quantitative comparison with state-of-the-at methods reference-based synthesis. The best score in each category is highlighted in bold.
Method
L1L
SSIM
LPIPS
FID
FontDiffuser
0.370
0.425
0.527
80.25
ChatGPT-5
0.201
0.331
0.412
55.50
Emic-4.5
0.375
0.507
0.475
68.38
Doubao
0.229
0.463
0.456
47.26
UniCalli
0.152
0.602
0,313
37.69
2
Table 4: Ablation study of our model's key compo- nents. Improved LPIPS and FID indicate enhanced di- versity and realism, avoiding the mode collapse associ- ated with pixel-wise averaging.
Pdrap' 0.200
0.020
0.000
Figure 5: Ablation study on Pdrop- An ex- cessively low Parop causes the model sac- rifice structural integrity to replicate long- tail styles, whereas an excessively high value leads to style-agnostic, canonical characters due to over-disentanglement
Metrie
L1.
SSIM
LPIPS
FID.
Baseline
0.200
0.551
0.430
52,90
+ Joint Training
0.160
0.604
0.387
46.42
+ RoPE Duplication
0.148
0.613
0.352
41.78
+
Cond. Dropout
0.152
0.602
0.313
37.69
Table 5: Quantitative comparison of UniCalli and OracleNet on Oracle Bone Script tasks. Genera- tion accuracy is evaluated by human experts and classified into three categories: Completely Correct, Largely Correct, and Completely Incorrect (detailed evaluation criteria are shown in Appendix G Recognition accuracy is measured against deciphered ground truth. N/A denotes that a method is not applicable to the task.
Generation Accuracy (%)
Method
Recognition Ace. (%)
Completely Correct
Largely Correct
Completely Incorrect
OracleNet UniCalli
N/A 20%
73.9%
67% %
13%
62.5%
oracle bone script characters from corresponding modern Chinese characters. Second, we evaluated its performance on supervised oracle bone character recognition, using the Top-K accuracy metric to quantify precision. For the third task, we applied our model to a set of 100 undeciphered oracle bone characters predict their potential modern Chinese character equivalents. The qualitative aspects of our study-namell, the generative task were evaluated by experts in oracle bone script from the School of History at Xiamen University, details in Appendix [ visual results are shown in Figure TO. Furthermore, we benchmarked our model against OracleNet (Zhou et al., 2025) on curated of 100 deciphered characters, as detailed in Table Recognition accuracy was calculated directly, while generation correctness was assessed by experts.
Egyptian hieroglyphs. We conducted experiments on dataset (Umer 2023 (see Appendix for more details).
5 CONCLUSION
We introduced UniCalli, unified diffusion framework that advances computational Chinese cal- ligraphy by unifying isolated character generation with holistic, page-level composition. Our con- tributions include a large-scale, annotated dataset and novel Multimodal Diffusion Transformer that jointly handles generation and recognition with global coherence. Trained on diverse data, Uni- Calli faithfully captures the stylistic nuances of master calligraphers, including complex ligatures and spatial rhythms, while maintaining strict glyph accuracy. The framework's 's robustness is shown by its successful extension to other ancient writing systems like Oracle bone script and Egyptian hicroglyphs, highlighting its potential as a powerful tool for the digital preservation and scholarly study of global cultural heritage.

---

Published as a conference paper at ICLR 2026
6 ETHICS STATEMENT
This research was conducted with the aim of preserving and promoting cultural heritage through computational methods. The dataset was created from digitized historical works, many of which are in the public domain, and augmented with synthetically generated data from publicly available fonts and literature. We believe this work has positive cultural impact by making the art of calligraphy more accessible. The code and dataset will be released publicly to encourage further academic research and creative applications in a responsible manner.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our research. This paper provides detailed description of our framework, UniCalli, the data collection and annotation process, and our exper- imental setup in Sections and To facilitate the verification of our results and to allow other researchers build upon work, we will make source code, the curated dataset with notations, and the pre-trained model weights publicly available upon publication, as stated in the abstract.
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang. Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding. Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang. Haiyang Xu, Junyang Lin, et al. Qwen2.5-v) technical report. https: 2025. We evaluate the 7B variant; accessed 2025-09-22.
//arxiv.org/anl abs/ 2502. 13233
Fan Bao, Shen Nic, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In CVPR, 2023a.
Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, and Jun Zhu. One transformer fits all distributions in multi-modal diffusion at scale. In International Conference on Machine Learning, pp. 1692-1717. PMLR, 2023b.
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprini arXiv:2311.15122 2023.
Zhifei Chen, Tianshuo Xu, Wenhang Ge, Leyi Wu, Dongyu Yan, Jing He, Luozhou Wang, Lu Zeng, Shunsi Zhang, and Ying-Cone Chen. Uni-renderer: Unifying rendering and inverse rendering via dual stream diffusion. In Proceedings of the Computer Vision and Pattern Recognition Confer- ence, pp. 86504-26513, 2025.
Gang Dai, Yifan Zhang, Quhui Ke, Qiangya Guo, and Shuangping Huang. One-dm: One-shot dif- fusion mimicker for handwritten text generation. In Proceedings of the European Conference on Computer Vision (ECCV), 2024. URL lttpt::://// //mp::://ww.... ecva. net /wtpt//ppppssccccceeeeppp 2024/
papers BCCV/papers/0779BB pdff
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghnni, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An im- age is worth 16x16 words: Transformers for image recognition at scale. International Confer- ence on Learning Representations (ICLR), 2021.
Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recogni- tion, pp. 12873-1288) 2021.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Corty-first international conference on machine learning, 2024.
10

---

Published as a conference paper at ICLR 2026
Lixue Gong, Xiaoxia Hou, Fanshi Li, Liang Li, Xiaochen Lian, Fei Liu, Liyang Liu, Wei Liu, Wei Lu, Yichun Shi, et al. Seedream 2.0: native chinese-enge -ngelihh bilingual image generation foundation model, 2025.
Haibin He, Xinyuan Chen, Chaoyue Wang, Juhua Liu, Bo Du, Dacheng Tao, and Yu Qiao. Diff-font: Diffusion model for robust one-shot font generation. International Journal of Computer Vision, 2024. doi: 10. 10.1007/s1 (00000/s1163-02---100 263-024-02137-0. URL htt.tttts///aiii.....
abs/2212 2212. 05895
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, and Luke Smith. Flux.1 kontext: Flow matching for in-context image generation and editing in latent space, 2025. URI. httpps:ss: http:////wwwwww...aaaa/aa /2556 15742
Qisheng Liao, Zhinuo Wang, Muhammad Abdul-Magedd and Gus Xia. Callipaint: Chinese cal- ligraphy inpainting with diffusion model. 2023. URL 01536
Qisheng Liao, Liang Li, Yulang Fei, and Gus Xia. Calliffusionv2: Personalized natural calligra- phy generation with flexible multi-modal control. 2024. URL httttt:/s///:xi..coo abs/ 0210.03781
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02747, 2022.
Kaiyuan Liu, Jiahao Mei, Hengyu Zhang, Yihuai Zhang, Xingjiao Wu, Daoguo Dong, and Liang He. Moyun: A diffusion- model for style-specific chinese calligraphy generation. 2024.
URL [RRppsts://axxi org/ http:/bb/2411....7 07618
Yitian Liu and Zhouhui Lian. Deepcallifont: Few-shot chinese calligraphy font synthesis by inte- grating dual-modality generative models. In Proceedings the Thirly-Eighth AAAI Conference
on Artificial Intelligence (AAAI 2024), pp. 3774-3782, 2024. doi: 10. 16.1/a0/aa vt.ii.....222.. 168.
URL wttpt:://soooo //ojs aaai www.oorgninnneeee http//nnexx.ecm www.ndx...ppppppppmm htt.://///aatttteeeeeeeeeeeeeeee
Yuxuan Luo, Jiaqi Tang, Chenyi Huang, Feiyang Hao, and Zhouhui Lian. Callireader: Contex- tualizing chinese calligraphy via umbedding-aligneee vision- vision-language model. 2025. URL
https://arxiv http:///////......../// 06443
Nihon Shuji. Nihon shuji official site: over 10,000 classrooms; about 300,000 leamners. Official website front page statement, 2025. URL 3022:/1LLLLttt/////ooooootttb
jp/ May 2024. Offi
OpenAl. Hello gpt-4o.
cial announcement of 4PT:4o; accessed 202essed22 2025-09-22.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4195 4205, 2023.
Baidu Research, Ernie 4.5 technical report. Technical report, Baidu, June 2025. Covers ERNIE 4.5 model family including VL variants; accessed 2025-09-22.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Baoguang Shi, Xiang Bai, and Cong Yao. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence, 39(11 11:2298 22224-224442. 2016.
Jiaming Song. Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502. 2020.
11

---

Published as a conference paper at ICLR 2026
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: En- hanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024.
Shusen Tang and Zhouhui Lian. Write like you: Synthesizing your cursive online chinese hand-
writing metric-based meta learning. Computer Graphics Forum, 40(2):441-51,, 2021. doi: 10. 142621. URL httpts://tgglibbb.....
Waleed Umer. Egyptian hieroglyphics datasets, 2023, URL https: http::/:/wwwwkkgglgee. com/
datassts
htt.://cweeeeeeeoo.oo....o.aaere aatsroosllppicccssdtttttttss datasets Accessed: 2025-
09-19.
Pengjie Wang, Kaile Zhang, Xinyu Wang, Shengwei Han, Yongge Liu, Jinpeng Wan, Haisu Guan, Zhebin Kuang, Lianwen Jin, Xiang Bai, et al. An open dataset for oracle bone character recogni- tion and decipherment. Scientific Data, 11(1):97, 2024.
Shan-Jean Wu, Chih- Yuan Yang, and Jane Yung-jen Hsu. Calligan: Style and structure-aware chi- nese calligraphy character generator. arXiv preprint arXiv:2005. 2020.
Yangehen Xie, Xinyuan Chen, Li Sun, and Yue Lu. Dg-font: Deformable generative networks
for unsupervised font generation. In Proceedings of the IEEE/CVF Conference Computer
thecvf. Vision and Pattern Recognition (CVPR), 2021. URL www.rmableeeeeeeeeee Generative. http///eeeee///e/11//aeeeeeeeeee com/content
Networks_foze Sor_sss__oottnnuuerreieeeeee ootpcppprrrraaaaooooeeeeenaooaoo wwceioon__v_______ paper________ppprr.......
pawpspapeepppp.. pdf
Zhenhua Yang, Dezhi Peng, Yuxin Kong, Yuyi Zhang, Cong Yao, and lianwen Jin. Fontdiffuser:
One-shot font generation via denoising diffusion with multi-scale content aggregation and style contrastive learning. In Proceedings of the AAAI conference on artificial intelligence, 2024.
Yangbo. Debate on "art grading" and reflections on aesthetic education, 03 2022. URL https:// shtml Quotes the Director of the Art Test Center, China Academy of Arts and Science of China (CASTI): 1.8 million participants in the institute's fine-arts grading exams in the previous year.
yangbo. www.wctwwoccccocccooooooooooooot httpb//.octtcccm/2222222222 2atoom/2222//00 A
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang. Vijay Vasudevan, Alexander Ku, Yinfei Yang, Bureu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu. Scaling autoregressive models for content-rich text-to-image generation. 2022.
Jinshan Zeng, Ling Tu, Jie Zhou, Yefei Wang, and Jiguo Zeng. Enhancing chinese calligraphy generation with contour and region-aware attention. In Proceedings of the 2023 International Joint Conference on Neural Networks (IJCN), pp. 1-9, 2023. doi: 10. 101100//NN555540.20000 10192011.
Liguo Zhang, Yalong Zhu, Achref Benarab, Yusen Ma, Yuxin Dong, and Jianguo Sun. Dp-font: Chinese calligraphy font generation using diffusion model and physical information neural net- work. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intel- ligence (IJCAI 2024), pp. 7796-7804, 2024. doi: 00.24963/jcaii2024423. 7/www. htt.:/oog//poceeegeee/e 2204//6332223233
URL https:
Yixin Zhao, Yuyi Zhang, and Lianwen Jin. Meed: A multi-attribute chinese calligraphy character dataset annotated with script styles, dynasties, and calligraphers. In International Conference Document Analysis and Recognition, pp. 538-555. Springer, 2025.
Shu Zhou, Xin Wang, et al. Oraclenet: Enhancing oracle bone script recognition with adaptive deformation and texture-structure decoupling. npj Heritage Science, 2025.
doi: 10. 10.100084440994055--13333322 URL https: http:://www... /tww://wwu........ot..m... nat 020---005--888-
12

---

Published as a conference paper at ICLR 2026
A WRITING ASSISTANCE (LLM USE DISCLOSURE)
We utilized a large language model (LLM), specifically ChatGPT, as a writing assistant to enhance the quality of this manuscript. The tool's role was strietly limited improving language. including grammar, clarity, and academic tone. All scientific content-includingg the core ideas, methodology, analyses, and experimental results wall was generated exclusively by the authors. We have carefully reviewed every modification suggested by the LLM to ensure it aligns with our original intent and maintains factual accuracy. The authors retain full responsibility for the final content of this paper.
B LIMITATIONS
The limitations of this work are twofold. Firstly, the historical calligraphy data contains considerable noise from age and poor preservation, which persists in the generated outputs despite our denoising efforts. Secondly, the severe long-tail distribution of the data, caused by the rarity of works from some calligraphers, is difficult to optimize algorithmically and results in deviations from the original artistic styles.
C DATA PRE-PROCESSING AND EXAMPLES
To simplify the learning task, all calligraphy images were preprocessed through denoising and bina- rization, and subsequently categorized by their background color (black white). To augment our dataset, we generated synthetic data using three TrucTTppe Font (TTF) files for Regular, Running, and Cursive scripts, rendering text from large corpus of classical and modern Chinese literature (e.g., Dream of the Red Chamber, Water Margin, and the collected works of Lu Xun). To ensure the model prioritized learning from authentic works, the sampling probability for synthetic data was set to 0.2 during training. Our conditioning prompts were structured into four parts: data source (real or synthetic), background color, calligrapher description, and script style description.
C.I
REAL IMAGE PROCESSING
Condition
Image
Box
Condition
Image
Box
Condition
Image
Box
3
Figure 6: Labeled data examples.
This stage processes high-resolution scans of historical calligraphy, which are accompanied by an- notations detailing the location and identity of each character.
Vertical Text Segment Extraction: Chinese calligraphy is traditionally written in vertical columns.
We first group the annotated characters into columns by clustering them based their horizontal
13

---

Published as a conference paper at ICLR 2026
coordinates. To create training samples of consistent length, filter for columns containing least a minimum number of characters, N (c.g.. .N = 5). From these valid columns, we randomly sample continuous vertical sequence of N characters. This process acts as form of random cropping, increasing data diversity.
Image Cropping and Augmentation: A tight bounding box is calculated around the selected char- acter segment, and the original image is cropped to this region with a small padding margin. As an optional data augmentation step, we employ an automatic binarization algorithm. This algorithm determines the image's polarity (light text on a dark background or vice-versa) by generating two candidate binary masks and scoring them based on two criteria:
Foreground Ratio: How well proportion of foreground pixels falls within predefined, typical range for text.
The degree to which Canny edges align with the interior of the foreground mask, indicating sharp, well-defined strokes. The higher-scoring mask chosen, normal- izing the image to white text on black background. This makes the model robust to variations paper and ink.
Edge Contrast:
C.2 SYNTIETIC DATA GENERATION
To augment our dataset and provide the model with a wider variety of styles and characters, we synthesize additional training samples.
Text and Style Sampling: A text segment of a random length (up to N) is sampled from a large corpus of Chinese literature. Concurrently, TrueType Font (TTF) is randomly selected from curated collection of diverse calligraphic fonts.
Image Rendering: The sampled text rendered onto blank canvas using the chosen font. The background and text colors (either black-on-white or white-on-black) are also selected randomly to ensure variability.
C.3 CONDITIONING SIGNAL FORMULATION
key aspect of our pipeline is the explicit separation of content and style into distinct conditioning signals.
Content Conditioning: For every sample, whether real or synthetic, we generate a content image. This is standardized representation where the target sequence of N characters is rendered in a sin- gle, consistent, non-stylized font (e.g., Regular/Kai) at fixed positions on a white background. This image serves unambiguous guide for "what" characters the model should generate, isolating content from stylistic attributes.
Style Conditioning: A descriptive text prompt constructed guide the artistic style. For real images, this prompt includes metadata such as the script type (c.g., Cursive, Seal), the author's 's name, and pre-defined stylistic descriptions associated with that author or script. For synthetic data, the prompt includes the name of the source TTF font and its associated style descriptors. All Chinese terms within the prompt are converted to Pinyin to maintain a consistent vocabulary.
Finally, all images (target, content) are resized to fixed resolution (e.g., 128 x 640) and normalized to a pixel value range of [-1, 1). The text prompt and the character sequence are tokenized for model consumption. The final output for each training step is tuple containing the target image, the content-con condiititnniiiinnng image, the style-conditioning prompt, and the tokenized character IDs.
To prepare model inputs, we process segments from the original source data through cropping and labeling. We first crop the data to isolate relevant regions of interest, as shown in the source examples in Figure 7 Each cropped segment is then annotated with ground-truth label. This creates the final curated dataset of labeled inputs, ready for model training. Examples of these final, labeled inputs are provided in Figure
14

---

Published as a conference paper at ICLR 2026
1
1
1
1レ4ト
1
1
+
1
1
11
14444
Figure 7: Dataset example.
C.4 ARTIFACTS GENERATED SAMPLES
Origin of Visual Anomalies. A qualitative examination of certain generated samples (e.g., styles corresponding to Sun Guoting and Yan Zhenqing) reveals the presence of irregular "white patches" or discontinuities within the stroke glyphs. We wish to clarify that these are not generative halluci- nations or model failures, but rather high-fideliyy reproductions of artifacts inherent to the training dataset.
Physical and Digital Causes. A significant portion of the training corpus is derived from historical stone stele rubbings (beike). These physical monuments have been subjected to centuries of nat- ural weathering, erosion, and mechanical damage, resulting in surface pitting and cracks. When these historical rubbings undergo digital binarization for dataset curation, these physical imperfec- tions-where the paper could not make contact with the eroded stone surface- are converted into binary "noise." " Consequently, the model, which aims to approximate the underlying data distribu- tion, learns to faithfully reconstruct these weathering artifacts as part of the stylistic texture.
15

---

Published as a conference paper at ICLR 2026
Future Mitigation. We acknowledge that while historically authentic, these artifacts may detract from the aesthetic cohesion of the generated calligraphy for certain applications. In future work, plan implement ngorous data-level preprocessing, utilizing dedicated denoising algorithms to restore the integrity of the glyph strokes before training, thereby separating the calligraphic content from the historical degradation of the substrate.
D EVALUATION ON PUBLIC BENCHMARKS
To ensure a fair and robust evaluation of our recognizer, we extended our experiments to include third-party public datasets. However, important to note that, to the best our knowledge, there are currently no publicly available datasets specifically dedicated to page-level Ancient Chi- nese Calligraphy. Existing public datasets in this domain primarily focus on isolated, individual characters Zhao et al. (2025).As the closest available alternative, selected CalliBench Luo et al. (2025), a recently released benchmark for page-level calligraphy. It is crucial to emphasize that while CalliBench supports page-level evaluation, its data distribution differs significantly from training objective: it predominantly consists of modern artistic fonts and contemporary calligraphy, rather than the historical ancient styles (e.g., Yan style, Cursive script) that UniCalli optimized for. This introduces a substantial domain gap between the training and testing distributions. Deaerbutions.Despite this challenge, we evaluated UniCalli on the CalliBench "Easy" subset against state-of- oftte-of--hearrr general- purpose Vision- Vision-Language Models (VLMs) and specialized OCR systems. The results are reported Table
As shown in Table 6 UniCalli achieves a competitive F1 score of 0.498. While specialized OCR models like GOT-OCR2.0 achieve higher performance- likely due their extensive training on broad, modern font datasets-UniCalli notably outperforms powerful general-purpose VLMs such as GPT-4o (F1: 0,400) and established industrial OCR systems like PP-OCRv5 (FI: 0.205). This result is encouraging: it demonstrates that although UniCalli trained strictly ancient historical data, has learned robust, generalized features that allow for effective zero-shot transfer to unseen modern artistic styles.
Table 6: Quantitative comparison of recognition performance on the CalliBench Luo et al. 2025) (Easy subset). Note that UniCalli trained exclusively on ancient historical calligraphy, whereas CalliBench consists largely of modern artistic fonts, introducing domain gap.
Model
Precision f
Recall
F1
NED I
Ernie-4.5- -5rr---r--To-L
0.542
0.481
0.482
0.637
Doubao-L.5-Thineee Thwihiing--iiinn-PPPPP Vision-Pro
0.442
0,513
0.462
0.655
PP-OCRv5
0.372
0.291
0.205
0.859
GPT-4o
0.457
0.403
0,400
0.726
Owen-2.5-VL-7B
0.440
0.736
0.534
0.710
GOT-OCR2.0
0.687
0,550
0.593
0.651
UniCalli
0.480
0.520
0.498
0.680
E IMPLEMENTATION DETAILS OF UNIFIED ARCHITECTURE
E. ZERO-SHOT TEXT RECOGNITION VIA FEATURE RETRIEVAL
Unlike traditional OCR systems that rely on autoregressive text decoding or classification heads, our unified framework performs recognition through zero-shot, retrieval-base mechanism. This ap- proach leverages the shared latent space hetween the generation and recognition tasks. The process consists of two phases:
1. Reference Library Construction (Offline):
We construct a comprehensive reference feature library L prior to inference. We render images for all standard characters c present in our vocabulary (derived from the standard f font file used in training). Each rendered character image is passed through the
16

---

Published as a conference paper at ICLR 2026
pre-trained VAE encoder & to obtain its latent feature representation ze:
L = {(c, Zo)
Ze = E(,),, Vc E V)
(4)
where V is the character vocabulary.
2. Inference and Matching:
During the recognition task, the model outputs predicted condition feature 2prd (the tensor immediately preceding the VAE decoder). To determine the text content, we compute the cosine similarity between &rred and all reference features in L. The recognized character è is determined by the nearest neighbor this latent space:
Zppeed Ze
argmax
(S)
| ||ze|l
This design avoids the need for auxiliary OCR module and ensures that the recognition capability is intrinsically aligned with the generative features of the model.
E.2 DUPLICATE ROPE WITH MODULATED EMBEDDING
To process heterogeneous inputs (Image, Condition, and Mask) within unified spatial context, employ Duplicate RoPE strategy augmented with Learnable Modulated Embeddings. This design ensures that the model can distinguish between input modalities without disrupting the spatial geometry defined by Rotary Positional Embeddings (RoPE).
Mechanism. Let X E R2xd represent the input sequence of features for a specific modality
m (Image, Condition, Mask). We initialize a unique, learnable embedding vector Vm F. for each modality, initialized to zeros. This vector is broadcasted across the sequence length and added element-wise the input features before the attention projection:
X
Xm Vm
Subsequently, the transformed features X are projected into Query (Q) and Key (K) states, to which RoPE applied.
Preservation of Relative Positioning. It is crucial to note that adding the modality embedding Vnn does not interfere with the relative positional encoding properties of RoPE.
Spatial Structure: RoPE injects positional information via rotation based the token index pos.
Modality Identity: The learnable embedding Vm acts as global, modality-specific "off- set" or bias in the semantic space.
By superimposing Vms we allow the model to distinguish what the input is (e.g., "this is a condition token" vs. "this is an image token")) while RoPE simultaneously tells the model where the input is. Since the two signals operate orthogonally- one static semantic bias and the other as dynamic frequency-based rotation-the underlying relative positional "grid" remains intact across all duplicated modalities.
F EGYPTIAN HIEROGLYPHS.
Directly translating from English to Egyptian hieroglyphs is unfeasible due to their fundamentally different structures. English utilizes a concise alphabetic seript, where small set of abstract sym- bols represents phonemes (units of sound). In stark contrast, the ancient Egyptian system is vastly more complex, employing hundreds of signs that function as logograms (signs for words), phono- grams (signs for sounds), and determinatives (semantic classifiers). A simple one-to-one phonetic or symbolic mapping between these systems therefore impossible.
To solve this challenge,
Chinese script semantic bridge in two-step process. The rationale is that both Chinese characters and Egyptian hicroglyphs share a logographic founda- tion, where symbols are often rooted in pictorial representations of concepts. This structural parallel
approach
17

---

Published as a conference paper at ICLR 2026
allows to bypass direct phonetic transliteration in favor of semantic-fire approach, mapping the core meaning of an English word rather than its sound. The complete pipeline is illustrated in Figure 8
By leveraging this intermediary, our method transforms an impossible phonetic transliteration into feasible conceptual and iconographic mapping. This creates more logical and meaningful bridge between the ancient and modern languages, preserving semantic intent.
the Kaggle-Egy dataset (Umer) 2023) to assess its performance both qualitatively and quantitatively. The qualita- tive results, presented in Figure showcase the high visual fidelity and contextual relevance of the generated hicroglyphs from English inputs. Concurrently, for a quantitative measure, the model's strong recognition accuracy summarized in Table 7 empirically confirming the method's 's effec- tiveness.
To validate framework,
conducted experiments
Generation
Recegitiin
Hieroglyphs
(Pictographic)
Chinese
(Pictgraphicc)
English
Board
(Alphabetic)
Soldier
One
Branch
game
Figure 8: An illustration of the mapping process from English to Egyptian hieroglyphs. Direct mapping is challenging due to the fundamental differences between English, an alphabetic script, and Egyptian hieroglyphs, pictographic script. To bridge this gap, our approach first maps English to Chinese-a script that is also fundamentally pictographic-wiee then serves as an intermediate representation for the final mapping hieroglyphs.
English
Soldier
One
Board
Mascot
Bandage
Small ring
Branch
Cooked
game
Ref.
Generation
English
Sandal
Eye
Brewer
Not
Corpse
Bread
Giraffe
Folded
Generation
Figure 9: Generation results of Egyptian hicroglyphs. The first row shows the input text in English. The second row is the reference image, and the third row displays the generated output.
18

---

Published as a conference paper at ICLR 2026
Table Accuracy of Egyptian hieroglyphs recognition.
Accuracy
UniCalli
0.96
Text
+
Y
Ref.
Gen.
Figure 10: Qualitative results of our model for Oracle Bone Script generation. For each example, we show the input modern Chinese character (top row), corresponding ground-truth glyph reference (middle row), and the Oracle Bone Script generated by our method (bottom row). Our generated results are highly consistent with the reference images in both structure and style.
G HUMAN EXPERT EVALUATION CRITERIA FOR GENERATION
This section details the criteria used by human experts evaluate the quality of the generated oracle bone script characters. Each generated character is assessed and categorized into one of three tiers based on its structural and stylistic accuracy compared the ground truth. The scoring guidelines follows:
The generated character is structurally identical the ground truth character or represents a well-accepted calligraphic variant. All strokes are correctly formed and placed.
Completely Correct:
Largely Correct:
The generated character captures the essential structure and is clearly recognizable, but contains minor inaccuracies. These may include incorrect stroke thick- ness, slight misplacement of components, or minor stylistic deviations that do not alter the character's 's identity.
The generated character is structurally flawed, unrecognizable as the target character, resembles a different character entirely.
Completely Incorrect:
Figure provides visual examples for each of these categories, illustrating the practical application of our scoring guidelines.
H USER STUDY OF GENERAL CALLIGRAPHY GENERATION
To evaluate the qualitative performance of our model, we designed a user study consisting of 10 questions. We recruited 20 participants, all of whom identified as calligraphy enthusiasts, com- plete the survey.
Each question, or evaluation task, presented participants with three components:
Prompt: textual prompt specifying the content be generated, a description of the target calligrapher's 's style, and the desired script style (e.g., Running Script, Clerical Script).
Reference Image: An image containing an authentic work excerpt from the specified cal- ligrapher serve ground-truth style example.
Generated Images: A set of calligraphy images generated by UniCalli and the baseline models for comparison. To prevent positional bias, the order of these images was random- ized for each question and each participant.
19

---

Published as a conference paper at ICLR 2026
Completely Correct:
Largely Correct:
Completely Incorrect:
Text
Ref.
Gen.
Figure 11: Examples illustrating the evaluation criteria for generated oracle bone script characters. Each row demonstrates samples categorized as (a) Completely Correct, (b) Largely Correct, and (c) Completely Incorrect by human experts.
Participants were asked to rate the generated images based on four key metrics: Style Fidelity, Glyph Accuracy, Naturalness, and Overall Preference. Ratings were provided on 5-point Likert scale, where I corresponds to "Worst" and 5 to "Best". The aggregated results, reported in the main paper, represent the mean and standard deviation of these ratings. An example of the user study interface is shown in Figure 12
MAR0AA ARE
Fd
QR. Anddd AHA
Qa. fied #et
Bys
Rydtyelly
Ovei
Seee
Figure 12: An example from our user study questionnaire.
H.1 QUALITATIVE FEEDBACK
In addition to quantitative ratings, we collected qualitative feedback from the participants. Below are some representative comments, translated from the original responses:
"The second option in QI (generated by UniCalli) is a very convincing replica. Huang Tingjian's 's style is often described as resembling 'dead snake hanging from a tree.' The other options devolved into an unorthodox and unrefined 'Jianghu' style were simply inaccurate."
Participant 1:
20

---

Published as a conference paper at ICLR 2026
Participant 2: "Regarding Q2, the second option exhibits an unrefined 'Jianghu' style and incorrectly uses simplified characters, leading to a distorted result. The first option is similarly unrefined. While the third option shows some resemblance to the style, it lacks structural accuracy. In comparison, the fourth option (generated by UniCalli) is signif- icantly better, though for running script, it could still improve the fluid connection and calligraphic "echo' between adjacent characters."
Participant 3: "Having practiced Yan Zhenqing's calligraphy myself, found that option 2 in Q7 (generated by UniCalli)
clear and striking match for his style."
Participant 4: "In Q8, the 'Slender Gold' style, characteristic of Emperor Huizong's s cal- ligraphy (Zhao Ji), was most accurately captured by the third option (generated by Uni- Calli)."
I MORE GENERATION RESULTS
This appendix provides
extensive showcase of our model's capabilities in calligraphic style generation. It is divided into two main parts. The first part presents the complete poem "Bring in the Wine" (Qiang Jin Jiu) by Li Bai, generated in the distinct styles of several master calligraphers. This expands upon the teaser in the main text, where only single line from each style was shown to form the poem collectively (see Figures 17 18 and [6). The second part features collection of names of famous ancient Chinese figures, rendered in various calligraphic styles (see Figures 15 13
14p. This demonstrates our UniCalli's generalization ability and its proficiency in handling Chinese characters of varying complexity.
#
#EF1
1
Condition
Synthetic (Hlegata
Sun Gurving (Cunvree
Holl
TB (EF) Kethi (Cunive)
Yan Zhenqing (Regalar)
UU (BR)
Zhknn Batn (Regalan)
#:B02 (B))
Zhao 3t (Regalar)
L8EERR BB (9B)
Houng Tingitan (Rouning)
HERS (fi)
Mi Fa (Rumning)
20:00 Ctt)
Zhao Mcngft (Ruoming)
35:21400 (fF)
Tang (Running)
HiW (FF)
IN WE (lf)
MorTTacciiiiv petcciittionss (Rogobar
(i))
Figure 13: Names of historical and mythological figures from Chinese culture, rendered in various calligraphic styles. From top bottom: Hua Mulan and Xiang Yu.
J APPLICATIONS IN CULTURAL AND CREATIVE INDUSTRIES
One of the primary motivations behind UniCalli is bridge the gap between state-of-the-ar gener- ative AI and traditional cultural heritage. Beyond academic metrics, the true value of calligraphy foundation model lies in its ability to empower designers and general users to create high-quality, aesthetically pleasing works for daily use with minimal technical barriers.
In this section, we showcase three specific types of cultural and creative products (Wen- (Wen-Chuang) designed using UniCalli. It is important to highlight that the creation process for these works was remarkably streamlined. The calligraphic content was generated by our model, while the specific column layouts and ink color conversions were automated using simple post-processing scripts. The
21

---

Published as a conference paper at ICLR 2026
+
11
2
2
Condition
(Huglarr)
Synthetic
Sun Guriin (Cunsve)
Wilt (BB)
Wanng Xicha (Curaive)
FBB: (RR)
Zhenojing (Regolar)
BR 08 (16)
Thang bothi (Ragalar)
1EB (I)
RS 1A- Si (8f) Zhoo B1 (egglaar
Houngg Tongiian (Running)
HURE (fF)
Fu (Rumning)
Hill ((G)
Thao Zhan Menglla (Rummay)
AE BE (fF)
Tang Vi (Rumiing)
MN (fF)
18 RIF (N tessenppiio (Regular)
Figure 14: Names of historical and mythological figures from Chinese culture, rendered in various calligraphic styles From top to bottom: Wang Zhaojun and Yang Jian.
A
Condition
Synthetic (Regular)
Sun Guaring (nantingCunnii (Cunsie)
PH (B))
Wang Niahi (Cunive)
Van Zhensjnng (Reuulr)
DR (WF)
BE Ziang Hehhi (Regban
NEW WK--BB Wo Zitoa In (Regalar)
Hungg Tongiao (Ruming)
RU (PP)
Mi Pu (Rumingg)
#WB (15)
Thoo Mangiu (Ranming)
RSS (fi)
HE (PF) Tung Yle (Rtriing)
Cliff lnscription (Rtegalary
1R MEG (10)
(W)
(B))
Figure 15: Names of historical and mythological figures from Chinese culture, rendered in various calligraphic styles. From top bottom: Tang Sanzang and Wukong.
final compositions were produced by compositing these generated assets onto decorative background images sourced from the open internet. These examples demonstrate not only the model's capability to generate authentic, high-resolution calligraphy but also the ease with which it can be integrated into practical design workflows.
J.1 CLASSICAL POETRY SCROLLS
The vertical column structure of Chinese calligraphy is most iconic in the presentation of classical poetry. We utilized UniCalli to generate complete scrolls of famous Tang and Song dynasty po- ems. The model maintains style consistency across multiple columns, capturing the flow (Qm) essential for artistic appreciation. Figure 19 presents classical example featuring Meng Haoran's 's renowned poem "Spring Dawn," demonstrating the model's ' ability to generate extended vertical compositions with proper character spacing and rhythmic balance.
22

---

Published as a conference paper at ICLR 2026
ラルメト
E (#)
Wang Xizhi (Cursive)
Figure 16: The full text of Li Bai's "Bring in the Wine" (Qiang Jin Jiu), generated in the calligraphic style of Wang Xizhi (Cursive).
J.2 BOOKMARKS AND AUSPICIOUS BLESSINGS
We also designed series of cultural bookmarks and stationery featuring "Auspicious Phrases" (Ji- Li-Hua)-shane lucky idioms used wish good fortune. Unlike fixed datasets, our text-to-image capability allows users to generate specific custom blessings in various artistic styles on demand, making them ideal for personalized gifts and souvenirs. Figures 20 23 showcase diverse calli- graphic styles applied to popular blessing phrases such as "Good Luck," "Great Fortune," "Rolling Wealth," and "Pass Every Test." These examples feature the distinctive styles master calligraphers including Mi Fu, Tang Yin, Zhao Ji, Zhao Mengfu, Sun Guoting, Wang Xizhi, and Huang Tingjian, demonstrating the model's versatility in style transfer while maintaining authentic character struc- tures.
J.3 TRADITIONAL MARRIAGE CERTIFICATES (HUN SHU)
In recent years, there has been a resurgence of interest in traditional Chinese marriage certificates (Hun Shu). These documents require a solemn, dignified, and elegant aesthetic. We applied UniCalli to generate customized marriage vows in multiple prestigious calligraphic styles. Figures 24|?? present a collection of marriage certificates rendered in the styles of Zhao Ji, Zhao Mengfu, Mi Fu, Tang Yin, and Zhang Jizhi. The solemn and majestic character structures perfectly suit the formal nature of the occasion, while the model's 's ability to maintain consistency across multiple columns ensures the ceremonial dignity of the documents.
J.4 SUMMARY OF IMPACT
These applications validate that UniCalli is not merely a style transfer tool but production-ready asset generation engine. By open open-soureing this model, we hope lower the barrier for designers to incorporate authentic Chinese calligraphy into modern cultural products, thereby fostering the revitalization of this intangible cultural heritage.
23

---

Published as a conference paper at ICLR 2026
*E79 (FF)
Mi Fu (Running)
#
 Ouyang Xun (Regulat)
IT (HE)
10 (93) Tang Yin (Running)
Figure 17: The full text of Li Bai's Bring in the Wine" (Qiang Jin Jiu), generated in the calligraphic style of Mi Fu (Running), Ou Yangxun (Regular), and Tang Yin (Running).
24

---

Published as a conference paper at ICLR 2026
BK: (H9)
Zhang Jizhi (Regular)
3U (HA)
Yan Zhenqing (Regular)
AS FE (HA) Zhao Ji (Regalar)
Figure 18: The full text of Li Bai's "Bring in the Wine" (Qiang Jin Jiu), generated in the calligraphic style of Zhang Jizhi (Regular), Yan Zhenqing (Regular), and Zhao Ji (Regular).
25

---

Published as a conference paper at ICLR 2026
7 (T)
Mr (fi) Tang Yin (Running)
Zhao Mengfu (Running)
10 Zhao Ji (Regular)
IEL (fF) Huang Tingjian (Running)
Figure 19: Meng Haoran
"Spring Dawn". Background images are sourced from the internet.
GOOD LUCK
GOOD LUCK
(a) Mi Fu Good Luck
(b) Mi Fu Wealth Coming
(c) Tang Vin Top Scholar
(d) Tang Vin Lose Weight
Figure 20: Cultural Creative Works Group Background images are sourced from the internet.
26

---

Published as a conference paper at ICLR 2026
GOOD LUCK
GOOD LUCK
GOOD LUCK
GOOD LUCK 6
Co C
#X
2 2
(a) Zhao
(b) Zhao Ji Peace
(c) Zhao Mengfu Great Fortune
(d) Zhao Mengfu Peace
Great Fortune
Figure 21: Cultural Creative Works
Group 2. Background images are sourced from the internet.
Sun Guoting Rolling Wealth
(b) Wang Xizhi Top the Exam
(c) Zhao Ji Pass Every Test
(d) Zhao Mengfu Fortune Turns
Figure 22: Cultural Creative Works Group 3. Background images are sourced from the internet.
H
A
A
(tb A
(a) Huang Tingjian Fortune Turns
(b) Mi Fu Top the Exam
(c) Mi Fu Rolling Wealth
(d) Tang Yin Pass Every Test
Figure 23: Cultural Creative Works Group 4. Background images are sourced from the internet.
27

---

Published as a conference paper at ICLR 2026
1 スラスガ
マルスコー
DE 22 2830
Figure 24: Zhao Ji Wedding Certificate Scries 1. Background images are sourced from the internet.
1
# ス
アルスコシ
OF 0830
Figure 25: Zhao Mengfu- - Wedding Certificate- - Series 1. Background images are sourced from the internet.
28

---

Published as a conference paper at ICLR 2026
(a) Mi Fu Wedding Certificate
(b) Tang Yin Wedding Certificate
Certiliie
Figure 26. Wedding Certificates. Background images are sourced from the internet.
29


Reviews:
[{"review_id": "MPlBYZvACc", "reviewer": "Reviewer_g4eJ", "summary": "This paper introduces UniCalli, a unified diffusion framework for column-level generation and recognition of Chinese calligraphy. The model jointly trains both tasks within a single diffusion transformer, where recognition constrains structural fidelity and generation provides stylistic and spatial priors. An asymmetric noising scheme enables bidirectional task switching, while rasterized box maps encode spatial structure for improved ligature and layout coherence. The authors also present a large-scale annotated dataset of over 8,000 calligraphic works. Experiments show that UniCalli achieves state-of-the-art generative fidelity and competitive recognition accuracy, and generalizes effectively to other ancient scripts, demonstrating its potential for comprehensive calligraphy synthesis and analysis.", "questions": "1.\tAs noted in the Weaknesses section, it remains unclear whether a single set of model weights is used for both generation and recognition tasks, or if two separate models are trained under a shared architecture. Additional details about the training strategy and task interaction (e.g., sequential vs. joint training) would be helpful.\n\n2.\tCould the authors provide an example of a text prompt used to specify style conditions? Clarifying this would help understand how style control is implemented in the model. \n\n3.\tFor the recognition task, if the model only generates glyph images rather than textual outputs, how is the character-level recognition accuracy in Table 2 computed? Are any external OCR tools used to convert generated glyphs into text for evaluation? Moreover, the models compared in Table 2 appear to be evaluated without task-specific training on this dataset, whereas the proposed model is specifically trained on it. This discrepancy makes the recognition comparison potentially unfair and may overstate the relative performance of the proposed approach. It would be helpful to compare the recognition performance of the proposed model with a model specifically trained on the same dataset.\n\n4.\tThe experiments on ancient scripts appear limited. For example, in Table 5, only OracleNet is used for comparison, and the recognition performance is worse than OracleNet. It would be informative to include comparisons of generative quality against other models, or to evaluate the effect of fine-tuning different diffusion backbones on generation performance", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The paper introduces a novel framework that unifies calligraphy generation and recognition within a single diffusion transformer. The use of asymmetric noising to switch between generation and recognition is both simple and effective, offering a principled way to couple dual tasks through shared latent representations.\n2. The authors contribute a large-scale, high-quality dataset comprising over 8,000 digitized works from 93 calligraphers, with detailed annotations that fill a long-standing gap in computational calligraphy research.\n3. The framework’s successful extension to other ancient scripts (e.g., Oracle bone inscriptions and Egyptian hieroglyphs) demonstrates its strong adaptability and generalization potential beyond Chinese calligraphy.", "weaknesses": "1.\tAlthough the paper claims that the generation and recognition tasks are trained within a unified framework, it remains unclear whether a single set of model weights performs both tasks or if two separate models are trained under a shared architecture. This ambiguity makes it difficult to assess the extent to which the proposed approach is genuinely unified rather than architecturally aligned.\nIf a single set of model weights is indeed shared, the paper does not specify the training schedule or task-balancing strategy—for instance, whether the model is trained sequentially (generation first, then recognition) or jointly with a particular ratio or alternation scheme. Moreover, there is a lack of ablation studies to substantiate the claimed complementarity between the two tasks. It would be valuable to compare a model trained solely on generation with one trained jointly on both generation and recognition tasks to verify that the dual-task setup yields mutual benefits rather than interference. Conversely, if two separate models are trained under a shared architecture, the framework can hardly be regarded as truly “unified.” In this case, the recognition capability appears limited compared with dedicated recognizers, as it produces only standard-font glyph renderings rather than textual outputs. This raises concerns about whether the proposed unification provides substantive advantages beyond a shared architectural backbone.\n\n2.\tBased on the visualizations presented in the paper, it appears that all examples show only a single column of five calligraphy characters. This limited demonstration raises concerns about the model’s claimed page-level generation capability and calls into question whether it can truly produce complete, multi-column calligraphic works.\n\n3.\tThe main text lacks a detailed description of how style conditions are incorporated. For a task that involves generating stylized calligraphy, precise control over style conditioning is crucial. From the appendix, it appears that style is introduced via text prompts rather than style reference images, which is not clearly stated in the main paper. This omission may be perceived as an important detail being underemphasized. Furthermore, this task formulation significantly limits the model’s generalization, as it cannot readily handle unseen style reference images; in essence, the model is constrained to generating fonts for which style labels are pre-defined, reducing its practical applicability in open-ended style transfer scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ySDBuphQlY", "reviewer": "Reviewer_QPYC", "summary": "This paper presents UniCalli, a diffusion-based model for Chinese calligraphy generation. During training, the model performs both generation and recognition tasks within the same architecture to enhance the learning of key structural features. To better realize column-level ligatures, the authors introduce an additional mask input and leverage shared positional information to improve spatial understanding.During experiments, the authors observe overfitting to long-tail calligraphers and propose random dropout to disentangle style and glyph representations. Overall, the paper is well organized, the results are convincing, and the experiments are sufficient, including evaluations on other ancient scripts.", "questions": "1.In Section 3.3, tc is dropped out under different conditions, but it is unclear whether this happens during the recognition task, the generation task, or both.\n2.In Figure 4, some generated examples (e.g., Sun Guoting, Yan Zhenqing, Cliff Inscriptions) contain white patches that do not belong to the calligraphic strokes. What is the cause of these artifacts?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.The proposed method is reasonable. By using the timestep ti to control the noise level, the model can indirectly alternate between recognition and generation, achieving joint enhancement of both tasks. This design also facilitates later dropout-based disentanglement.\n2.The experiments are comprehensive, including comparisons with current state-of-the-art multimodal models and other ancient scripts.", "weaknesses": "1.Some design choices lack clear justification, such as the use of RoPE and MMDiT. It would be helpful to explain why these particular architectures are necessary for this task.\n2.Although the ablation study is relatively detailed, its progressive setup makes certain conclusions less clear. For example, when dropout is combined with RoPE duplication, performance decreases—would applying dropout alone (without RoPE duplication) lead to better results?\n3.The ablation study does not examine the value or distribution of tc. Since the model jointly trains recognition and generation by mixing content images and masks, the selection of tc may play an important role in model performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0gDEmYkZYI", "reviewer": "Reviewer_ucuD", "summary": "This paper proposes UniCalli, which unifies column-level generation and recognition of Chinese calligraphy in a single diffusion framework. It is evaluated on datasets of Chinese characters, oracle bone scripts, and Egyptian hieroglyphs.", "questions": "Please refer to Weaknesses 1-6.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tThis paper analyzes the column-level text recognition and generation tasks and proposes a unified framework.\n2.\tThis paper presents a large-scale column-level calligraphy annotation dataset.", "weaknesses": "1.\tIn this paper, \"column-level generation\" refers to the generation of a single column of calligraphy image, which is fundamentally different from the \"page-level generation\" emphasized in the introduction section. Please clarify this distinction.\n2.\tIn Section 3.3, this paper uses conditional dropout addresses the issue of overfitting to long-tail calligraphic styles. Could you provide a more comprehensive explanation of this issue, as well as the motivation of this module?\n3.\tIn the experiments, the paper uses several general models (such as ChatGPT-5, Ernie-4.5, and Doubao). Would it be possible to include additional calligraphy generation method like [1] for comparison?\n4.\tThis paper claims that coupling recognition and generation tasks can improve the model's performance on both tasks, but lacks effective ablation experiments to validate this claim. It is recommended to conduct separate ablation experiments for the generation and recognition modes to demonstrate it.\n5.\tThe ablation experiments lack a clear description of the baseline setup.\n6.\tIt is recommended to provide visualizations to clearly illustrate the impact of RoPE Duplication module?\n\n[1] Dp-font: Chinese calligraphy font generation using diffusion model and physical information neural network, IJCAI, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vY4iJVatKe", "reviewer": "Reviewer_ct53", "summary": "This paper tackles the challenges of data scarcity, long-tailed style distribution, and the quality trade-off between character-level fidelity and page-level coherence in Chinese calligraphy generation and recognition. Firstly, the work constructs a curated, annotated calligraphy dataset, providing a valuable resource for page-level research. Concurrently, the study proposes the UniCalli framework, which synergistically trains generation and recognition as mutually beneficial dual tasks. The framework incorporates a non-autoregressive global planning architecture and introduces a rasterized bounding-box map to enhance the model's spatial reasoning capabilities. Experimental results indicate that these strategies have achieved a performance improvement.", "questions": "1.The experimental section only presents results on single-column data. A significant question arises regarding the generalizability of the proposed method: can it be effectively applied to the generation and recognition of multi-column layouts, which are common in actual page-level documents? If not, the claim that the method is suitable for page-level tasks appears overstated and requires qualification.\n\n2.The process by which the recognizer converts the Condition Image into text is unclear. Does the pipeline incorporate an off-the-shelf OCR module? Please clarify the specific implementation details.\n\n3.In the 'Duplicate RoPE with Modulated Embedding', how are the learnable modulation embeddings added to the RoPE? I am confused as to whether doing so will damage the relative positional encoding function of RoPE.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.A primary strength is the construction of a annotated dataset of classical Chinese calligraphy works. This resource is valuable and provides a substantial asset for subsequent research in page-level calligraphy analysis and generation.\n\n2.The introduction of asymmetric noising to integrate generation and recognition into a single, bidirectional framework forms the core contribution of this work. This unified approach is shown to enhance the performance of both tasks, as evidenced by the experimental results.", "weaknesses": "1.Unclear writing and definitions: The introduction does not clearly articulate the logical relationship between the two stated challenges: (1) scarce data and a long-tail distribution of styles; (2) the inability of existing methods to generate both high-quality individual characters and coherent full-page layouts. Additionally, the concepts of \"page-level\" and \"column-level\" are used interchangeably without a clear definition of the proposed method's application scope.\n\n2.Incomplete methodological description: Firstly, the workflow of the recognizer is incomplete. According to the methodology section, the recognizer only generates a Condition Image rather than text, leaving it ambiguous how the final text output is actually derived from this image. Secondly, regarding the \"Duplicate RoPE with Modulated Embedding,\" the description does not specify how the learnable modulation embeddings are added to RoPE (e.g., applied to 'Q' or 'K'). This lack of detail raises concerns about whether the operation might disrupt RoPE's relative positional encoding functionality.\n\n3.Insufficient Comparative Baselines: The experimental comparisons for the generation task are restricted to only one specialized calligraphy generation method (FontDiffuser), with the remaining baselines being general-purpose models. Similarly, the recognition task is evaluated solely against general-purpose models. It is recommended to include comparisons with more specialized methods, such as CalliffusionV2 for generation and CalliReader for recognition, as mentioned in the related work.\n\n4.Potential for Data Bias and Lack of Generalization: The recognition accuracy comparison in Table 2 may not be entirely fair. Since the test set is derived from the authors' proprietary dataset, UniCalli's superior performance could be attributed to its prior exposure to a similar data distribution during training, an advantage not shared by the baseline models. Although the authors appropriately acknowledge this potential limitation in line 403, it would be advisable to supplement the evaluation with performance benchmarks on established public calligraphy datasets. Such an addition would help ensure an equitable comparison and more conclusively demonstrate the recognizer’s robustness. \n\n5.Inconsistency Between Generated Results and Descriptions: A qualitative analysis of Table 6 reveals a potential issue: the second generated image appears visually inconsistent with its reference image.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)