Computational replication of Chinese calligraphy remains challenging. Existing methods falter, either creating high-quality isolated characters while ignoring page-level aesthetics like ligatures and spacing, or attempting page synthesis at the expense of calligraphic correctness. We introduce \textbf{UniCalli}, a unified diffusion 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 fosters concept-level abstractions that improve both tasks, especially in limited-data regimes. We curated a 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 a mix of synthetic, labeled, and unlabeled data. The model achieves state-of-the-art generative quality with superior ligature continuity and layout fidelity, alongside stronger recognition. The framework successfully extends to other ancient scripts, including Oracle bone inscriptions and Egyptian hieroglyphs. Code and data can be viewed in \href{https://github.com/EnVision-Research/UniCalli}{this URL}.

翻译：中文书法的计算复现仍然具有挑战性。现有方法存在不足，要么在生成高质量单字时忽略了连笔与间距等页面级美学特征，要么试图进行页面合成却牺牲了书法的正确性。我们提出了 **UniCalli**，一个用于列级识别与生成的统一扩散框架。将两项任务联合训练是经过深思熟虑的：识别任务约束生成器以保持字符结构，而生成任务则提供风格与布局先验。这种协同作用促进了概念级抽象，从而提升了两项任务的性能，尤其是在数据有限的场景下。我们整理了一个包含超过8,000件数字化作品的数据集，其中约4,000件进行了密集标注。UniCalli采用非对称噪声处理和栅格化的边界框图来获取空间先验，并在合成数据、标注数据及未标注数据的混合数据集上进行训练。该模型在生成质量上达到了最先进水平，具有更优的连笔连续性和布局保真度，同时识别能力也更强。该框架已成功扩展到其他古文字，包括甲骨文和埃及象形文字。代码和数据可在 \href{https://github.com/EnVision-Research/UniCalli}{此链接} 中查看。