Decoupling Layout from Glyph in Online Chinese Handwriting Generation

Authors: Minsi Ren, Yan-Ming Zhang, yi chen
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: online handwriting generation; layout generation; calligraphy imitation; conditional diffusion model

Abstract:
Text plays a crucial role in the transmission of human civilization, and teaching machines to generate online handwritten text in various styles presents an interesting and significant challenge. However, most prior work has concentrated on generating individual Chinese fonts, leaving complete text line generation largely unexplored. In this paper, we identify that text lines can naturally be divided into two components: layout and glyphs. Based on this division, we designed a text line layout generator coupled with a diffusion-based stylized font synthesizer to address this challenge hierarchically. More concretely, the layout generator performs in-context-like learning based on the text content and the provided style references to generate positions for each glyph autoregressively. Meanwhile, the font synthesizer which consists of a character embedding dictionary, a multi-scale calligraphy style encoder and a 1D U-Net based diffusion denoiser will generate each font on its position while imitating the calligraphy style extracted from the given style references. Qualitative and quantitative experiments on the CASIA-OLHWDB demonstrate that our method is capable of generating structurally correct and indistinguishable imitation samples.

Decision: accept (poster)
Reviewer scores: 6, 6, 5, 8

Reviews and discussion:
<thread>
creation date:
1730468766548

thread_type:
official_review

<thread_object>
creator: reviewer_up7t

<content>
summary:
The paper addresses the task of generating online handwritten Chinese text lines condition on the content and style. It identifies that text lines can be divided into two components: layout and characters. The authors propose a hierarchical approach that includes a text line layout generator and a stylized font synthesizer. The layout generator uses in-context-like learning to determine the positions of each character, while the font synthesizer generates characters that imitate the calligraphic style of the provided references. The method is evaluated using the CASIA-OLHWDB dataset, demonstrating its effectiveness in producing structurally correct and indistinguishable imitation samples.

strengths:
1.While some work on English handwritten text line generation exists, as far as I know, no such work has been published for online Chinese text lines. Compared to English characters, Chinese characters have more complex structures and a larger number of categories, making English generation methods unsuitable for direct application to Chinese. This work proposes a method to address this task, representing a noticable contribution.

2.The method decouples text line generation into two steps—layout generation and character generation—under a unified probabilistic framework, providing a good theoretical foundation and considerable novelty.

3.The experimental section includes comprehensive comparative and visualization experiments for both layout generation and character generation, yielding convincing results.

4.The paper is well-organized and clearly written.

weaknesses:
1. The assumption that character generation is independent given their positions seems too strong. Does text line style only manifest in the relative positions and sizes of individual characters? I hope the authors can give discussion on the reasonableness of this assumption and explain whether it might limit the method's ability in style learning. 

2. It is better to add sub-figure index for figure 8 and 9. It seems each of figure 8 and 9 has three sub-figures, but now their boundaries are not clear. In Figure 7, it is also suggested to identify which one is the proposed method in the paper. Of course, this is not a big issue.

questions:
1. If the bounding box generated by the layout model and the bounding box generated by the character model have different shapes, how should this be handled?

2. Since the method can be described as a unified probability distribution according to Equation 1, why not jointly train the two models end-to-end instead of training them separately?

3. The paper does not discuss whether the method can be applied to handwriting generation of other languages.

4. in Line 285, what does L represent? Although the authors write this is the length of the feature sequence, it is not clear what does this sequence represent? 

5. In Line 230, is the ground truth the same as the reference input in Figure 3？
</content>
</thread_object>

</thread>
<thread>
creation date:
1730517673739

thread_type:
official_review

<thread_object>
creator: reviewer_bu7e

<content>
summary:
This paper focuses on the generation of online Chinese handwritten text lines. The core of this method lies in decomposing text line generation into layout generation and character generation, and fill characters into the generated layouts to form complete text lines. Experiments evaluate the proposed method.

strengths:
1) This paper proposes a hierarchical online Chinese handwritten text line generation method. The proposed method utilizes a layout generator and a font synthesizer to produce the layouts and characters independently, then arranges the characters within the layouts to create complete text lines.

2) The proposed method achieves the best performance in purely data-driven font generation task.

weaknesses:
1) The multi-scale style encoder is not a new design in handwriting generation area, as a similar idea has been proposed in [a]. Besides, the proposed style contrastive learning loss is somewhat similar to the style learning loss in [b].

2) The method description is not clear: (1) In lines 233-237, it is mentioned that style reference samples are used as context prefixes, but how they guide the subsequent layout generation is unclear. (2) The paper does not specify the modality of the style references used, online data, or offline images. (3) The paper does not specify the number of style reference samples used, one-shot or few-shot.

3) Section 4.3.2 lacks quantitative experiments in terms of calligraphy styles, raising doubts about whether the proposed Multi-Scale Style Encoder can accurately extract calligraphy styles from entire text lines.

4) In the 'Conditional' row of Figure 7, the generated layouts (red boxes) show significant absences at the beginning of the text line, which raises concerns about the effectiveness of the layout generator.

5) It is recommended to compare the proposed method with style transfer-based approaches, as it can be relatively straightforward to extend this method to a style transfer setting by replacing character embedding with a CNN-based content encoder.

6) The layout generator requires real layouts of style references, which is not directly available in the application, does this limitation affect its applicability? If some simple layout extraction methods are used to extract the pseudo-layouts of style references, what impact would this have on generation performance?

7) The paper provides very few generated visual results and lacks visual comparisons with the baseline.

[a] Wang H, Wang Y, Wei H. Affganwriting: a handwriting image generation method based on multi-feature fusion, ICDAR, 2023.

[b] Dai G, Zhang Y, Wang Q, et al. Disentangling writer and character styles for handwriting generation, CVPR, 2023.

questions:
My main concerns are the novelty of the proposed multi-scale style encoder and style contrastive learning loss, and the effectiveness of the proposed layout generator. For details, please refer to the weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730647054727

thread_type:
official_review

<thread_object>
creator: reviewer_kxe9

<content>
summary:
This paper focuses on the generation of online Chinese handwriting text lines. It proposes a hierarchical approach that decouples layout generation from glyph generation. The text line layout generator arranges character positions based on text content and writing style references, while the font synthesizer generates characters with specific styles. The contributions include a novel layout generator, a 1D U-Net network for font generation, and a multi-scale style encoder. Experiments demonstrate the effectiveness of the method in generating structurally correct and stylistically similar samples.

strengths:
(1) The hierarchical decomposition into layout and glyph generation is an innovative approach, particularly suited for complex scripts like Chinese. This framework successfully addresses challenges specific to the language, such as the diversity of character structures.

(2) The model is thoroughly tested on both character and line generation, with metrics tailored to layout and stylistic fidelity. The model's success across multiple metrics shows a well-rounded, effective design.

(3) Despite the technical depth, the paper provides a good level of explanation for each module, with helpful visualizations that demonstrate layout and glyph generation separately.

(4) The method has potential applications in handwriting synthesis, digital personalization, and document augmentation, contributing a valuable approach for future research in multilingual handwriting generation.

weaknesses:
(1) Missing qualitative comparisons with prior methods, limiting insights into this model’s advantages in style fidelity and layout accuracy.

(2) The contributions over previous approaches could be articulated more clearly, especially regarding the effectiveness of the layout-glyph separation.

(3) The organization could be refined for readability, as the methods section contains complex explanations that could benefit from clearer structuring.

questions:
(1) Could more details be provided on how the layout-glyph separation specifically enhances performance in comparison to prior models?

(2) Would additional experiments on style consistency across diverse text lines clarify the benefits of this approach?

(3) Could this method can be adapted to non-Chinese scripts or connected handwriting styles?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730712018922

thread_type:
official_review

<thread_object>
creator: reviewer_ic1s

<content>
summary:
The paper introduces a novel approach for generating online handwritten Chinese text with specific styles. The authors naturally divide a text line into two components: layout and glyphs, and design a text line layout generator coupled with a diffusion-based stylized font synthesizer to address this challenge hierarchically. The layout generator autoregressively generates the positions for each glyph based on text content and provided style references, while the font synthesizer generates each font at its position while imitating the calligraphy style extracted from the given style references. Experiments on the CASIAOLHWDB demonstrate the method's capability to generate structurally correct and indistinguishable imitation samples.

strengths:
1. The study proposes a hierarchical method to address the under-explored task of online handwritten Chinese text line generation.
2. By decoupling layout generation from glyph generation, the method offers more flexibility in handling the generation of text lines, which is particularly useful when dealing with complex Chinese characters.
3.  The experiments conducted on the CASIA-OLHWDB database indicate high performance in imitation sample generation, demonstrating the effectiveness of the method.

weaknesses:
1. While decoupling layout and glyph generation increases flexibility, it may also add to the model's complexity, potentially affecting training and inference efficiency.
2. Are there any application scenarios for this task? The author could analyze its practicality.
3. The paper mentions difficulties in imitating styles with extensive cursive connections between characters due to the independent generation of each character, indicating potential limitations in handling certain calligraphic styles.

questions:
Please see Weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1733289873205

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
The summary of rebuttal

comment:
Dear AC and reviewers,

In this section, we aim to highlight the contributions of our work and summarize the key points from our rebuttal. Our study proposes a hierarchical approach to solve the challenging task of handwritten Chinese text line generation, which has been rarely explored. We introduce a in-context layout generator based on the next-token-prediction paradigm and construct a 1D-UNet-based diffusion denoiser for font generation. Our method can also be applied to other complex structured handwritten data and is suitable for use as a data augmentation technique that generates data with strong labeling information. 

During the rebuttal stage, we are pleased that the reviewers have acknowledged the novelty and contribution of our methods. We provided detailed responses to the reviewers' questions regarding the method's specifics. Following their suggestions, we not only improved our manuscript, but also added quantitative and qualitative experiments in the appendix and supplementary materials to further validate the effectiveness and scalability of the method. We believe that these revisions adequately address all the reviewers' concerns and further strengthen the contributions of our study.

We sincerely appreciate the time and effort that the reviewers have dedicated to evaluating our manuscript. Their insightful comments and constructive feedback have significantly improved the quality of our work. We are also grateful for the AC's efforts throughout the rebuttal process.

Warm regards,

The Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper studies online handwritten Chinese text generation. After rebuttal, the overall rating of this paper is above the marginal acceptance threshold, but with mixed scores of 5,6,6,8. The Area Chair has read the paper, all reviews, and the authors' rebuttal. The main strengths of the paper include 1) the task of the online handwritten Chinese text is considered to be important and the proposed method is well motivated, 2) the proposed decoupling of layout and glyph is reasonable and innovative, 3) the experimental design is thorough, and the results presented are convincing.

During the rebuttal phase, the authors addressed most of the reviewers' concerns, leading Reviewers iC1s, Up7T, and kxE9 to lean toward accepting the paper. However, Reviewer BU7e remained concerned about the novelty of the proposed multi-scale style encoder with contrastive loss, maintaining a rating of 5. Despite this, considering all review comments and the paper's other contributions, the AC agrees that the paper meets the acceptance threshold. Additionally, during the discussion phase, Reviewer BU7e also agrees that the paper could be accepted despite the concerns regarding its novelty.

The recommendation is acceptance. The authors are advised to carefully revise the manuscript by incorporating the reviewers' suggestions to enhance its quality further.