摘要： 针对少样本学习场景下中文书法字符智能生成技术普遍存在的核心瓶颈，包括精细笔画细节表征缺失、汉字间架结构错位畸变、传统书体风格语义表达不足、生成字符可读性与规范性不稳定等技术难题，本研究提出一种面向多风格中国书法字符的精细化可控生成方法（MSCDiff: A Controllable Generation Method for Multi-Style Chinese Calligraphy）。该研究以解决少样本条件下书法生成的风格保真、结构严谨、细节还原与内容可靠为核心研究目的，致力于突破现有生成模型在传统书法艺术特征建模上的局限性，实现兼具艺术表现力与字形规范性的多风格书法字符自动化生成，为中华书法数字化传承、智能创作与风格化文字渲染提供高效可行的技术方案。 在实现方法与核心技术层面，本方法以条件扩散生成模型为基础架构，摒弃单一条件约束模式，构建多维度协同条件注入机制，将目标字符内容语义、参考书法风格特征、高频笔触细节特征共同作为生成约束条件，从底层增强模型对书法字符的风格迁移能力、结构约束能力与细节刻画能力。为精准捕捉书法艺术中飞白、顿挫、边缘锐度等细粒度风格特征，专门设计高频特征提取模块，通过高频信息先验构造、可学习特征编码与细节特征增强策略，强化模型对书法笔触边缘轮廓、飞白纹理质感、局部笔画顿挫等关键艺术特征的表征与还原能力，解决传统模型对精细艺术细节建模模糊、风格表达扁平化问题。为保障汉字字形结构严谨性与稳定性，克服少样本下结构错位、部件畸变缺陷，引入标准印刷字库结构映射机制与专用内容编码器，精准提取目标字符的骨架结构与间架布局特征；在此基础上构建多层次特征融合机制，将内容结构特征、全局风格特征、高频笔触特征进行分层耦合与自适应融合，将融合后的综合约束信息有序注入扩散模型的去噪生成全过程，实现结构约束、风格约束与细节约束的协同调控。为进一步提升生成结果的语义准确性与可读性，杜绝错字、漏笔画、结构变形等规范性问题，创新性引入字符纠错模块，依托连接时序分类（CTC）损失函数构建语义监督反馈机制，将字符语义合规性作为反向优化信号引导生成网络迭代优化，从语义层面约束生成过程，显著提升生成字符的内容准确性与实用可靠性。 为支撑模型训练与全面验证，本研究自主构建高质量中文书法专用数据集Calli，该数据集涵盖11位书法家的代表性书写风格，包含超过7万张高精度、标准化单字符图像，样本标注规范、风格覆盖全面，为少样本多风格书法生成任务提供了高质量实验基础。研究在自建Calli数据集与公开IAM手写数据集上开展多组对比实验与消融实验，从主观视觉效果、客观量化指标、泛化性能等维度进行综合评估。实验结果表明，本研究提出的MSCDiff方法能够稳定生成笔画细节完整、间架结构严谨、书体风格鲜明、可读性优异的中文书法字符；在少样本学习设定下，模型在风格保真度、结构准确率、细节还原度与语义一致性等多个方面取得了较优的综合性能，在多数评价指标上优于现有主流生成方法，有效解决了少样本场景下书法生成的细节缺失、结构错位、风格表达不足等痛点问题，展现出优异的生成有效性、泛化能力与实用价值。该方法不仅实现了多风格书法字符的高精度可控生成，更为传统艺术数字化、智能内容生成与少样本视觉建模领域提供了可借鉴的技术路径。

Abstract: Aiming at the core bottlenecks in intelligent generation of Chinese calligraphy characters under few-shot scenarios, including the lack of detailed stroke features, distorted character structures, insufficient style expression and unstable readability of generated contents, this study proposes a refined controllable generation method for multi-style Chinese calligraphy (MSCDiff: A Controllable Generation Method for Multi-Style Chinese Calligraphy). The core research purpose is to break through the limitations of existing generation models in modeling traditional calligraphic artistic features under few-shot conditions, realize the automatic generation of multi-style calligraphy characters with both artistic expression and standard glyph structure, and provide an efficient and feasible technical solution for the digital inheritance, intelligent creation and stylized text rendering of Chinese calligraphy. In terms of implementation methods and key technologies, the proposed method takes the conditional diffusion model as the basic architecture and abandons the single conditional constraint mode. A multi-dimensional collaborative conditioning injection mechanism is constructed, which takes the target character content semantics, reference calligraphy style features and high-frequency stroke detail features as joint generation constraints, so as to enhance the model’s capability of style transfer, structural constraint and detail depiction for calligraphy characters from the bottom layer. To accurately capture fine-grained stylistic features such as flying white, stroke transitions and edge sharpness in calligraphy, a high-frequency feature extraction module is specially designed. Through high-frequency prior construction, learnable feature encoding and detail feature enhancement strategies, it strengthens the model’s representation and restoration of key artistic features such as stroke edges, flying white textures and local stroke transitions, solving the problems of vague fine artistic details and flattened style expression in traditional models. To ensure the rigor and stability of Chinese character structures and overcome the defects of structural dislocation and component distortion under few-shot conditions, a standard font library structure mapping mechanism and a dedicated content encoder are introduced to accurately extract the skeleton and layout features of target characters. On this basis, a multi-level feature fusion mechanism is built to hierarchically couple and adaptively fuse content structure features, global style features and high-frequency stroke features, and inject the integrated constraint information into the denoising generation process of the diffusion model in an orderly manner, realizing the collaborative regulation of structural, stylistic and detail constraints. To further improve the semantic accuracy and readability of generated results and avoid normative defects such as wrong characters, missing strokes and structural deformations, a character error correction module is innovatively introduced. A semantic supervision feedback mechanism is constructed based on the Connectionist Temporal Classification (CTC) loss, which takes the semantic compliance of characters as a reverse optimization signal to guide the iterative optimization of the generation network, significantly improving the content accuracy and practical reliability of generated characters from the semantic level. To support model training and comprehensive verification, this study independently constructs a high-quality dedicated Chinese calligraphy dataset Calli, which covers the representative writing styles of 11 calligraphers and contains more than 70,000 high-precision and standardized single-character images with standardized annotations and comprehensive style coverage, providing a high-quality experimental basis for few-shot multi-style calligraphy generation tasks. Multiple comparative and ablation experiments are carried out on the self-built Calli dataset and the public IAM handwriting dataset, with comprehensive evaluation from the dimensions of subjective visual effect, objective quantitative indicators and generalization performance. The experimental results show that the proposed MSCDiff method can stably generate Chinese calligraphy characters with complete stroke details, rigorous glyph structures, distinct calligraphic styles and excellent readability. Under the few-shot setting, the model achieves superior comprehensive performance in multiple aspects, including style fidelity, structural accuracy, detail restoration, and semantic consistency. It outperforms existing mainstream generation methods on most evaluation metrics, effectively alleviating the issues of missing details, structural misalignment, and insufficient style expression in calligraphy generation under few-shot scenarios, demonstrating excellent generation effectiveness, generalization ability, and practical value.This method not only achieves high-precision controllable generation of multi-style calligraphy characters, but also provides a referable technical path for the fields of traditional art digitization, intelligent content generation and few-shot visual modeling.