AMR-CCR: 以锚定模块检索实现连续中文字符识别
AMR-CCR: Anchored Modular Retrieval for Continual Chinese Character Recognition
AI总结：
AMR-CCR通过锚定模块检索框架实现连续中文字符识别，解决持续类增长和脚本多样性问题，提出轻量级脚本注入模块和多原型字典以提升识别性能。
摘要：
古代中文字符识别是文化遗存数字化的核心能力，但现实工作流程本质上是非静态的：新发掘的材料持续被加入，带来不同字体的新类别，随时间扩展类别空间。我们将其过程形式化为连续中文字符识别（Continual CCR），一种脚本阶段、类别递增的设置，结合两个挑战：（i）在持续类增长下进行可扩展学习，具有细微的类间差异和稀缺的增量数据；（ii）由于书写风格在不同作家和载体条件下的变化，导致明显的类内多样性。为克服传统封闭集分类的局限，我们提出AMR-CCR，一种以锚定模块检索框架，通过基于嵌入的字典匹配在共享多模态空间中进行识别，允许通过简单扩展字典来添加新类别。AMR-CCR进一步引入一个轻量级的脚本条件注入模块（SIA+SAR），以校准新加入的脚本，同时保持跨阶段嵌入兼容性，并引入一个基于图像的多原型字典，通过聚类类内嵌入以更好地覆盖多样的风格模式。为支持系统评估，我们构建了EvoCON，一个六阶段基准测试，涵盖六个脚本（OBC、BI、SS、SAC、WSC、CS），并附加了意义/形状描述和一个显式的零样本分割，用于没有图像示例的未见字符。
英文摘要：
Ancient Chinese character recognition is a core capability for cultural heritage digitization, yet real-world workflows are inherently non-stationary: newly excavated materials are continuously onboarded, bringing new classes in different scripts, and expanding the class space over time. We formalize this process as Continual Chinese Character Recognition (Continual CCR), a script-staged, class-incremental setting that couples two challenges: (i) scalable learning under continual class growth with subtle inter-class differences and scarce incremental data, and (ii) pronounced intra-class diversity caused by writing-style variations across writers and carrier conditions. To overcome the limitations of conventional closed-set classification, we propose AMR-CCR, an anchored modular retrieval framework that performs recognition via embedding-based dictionary matching in a shared multimodal space, allowing new classes to be added by simply extending the dictionary. AMR-CCR further introduces a lightweight script-conditioned injection module (SIA+SAR) to calibrate newly onboarded scripts while preserving cross-stage embedding compatibility, and an image-derived multi-prototype dictionary that clusters within-class embeddings to better cover diverse style modes. To support systematic evaluation, we build EvoCON, a six-stage benchmark for continual script onboarding, covering six scripts (OBC, BI, SS, SAC, WSC, CS), augmented with meaning/shape descriptions and an explicit zero-shot split for unseen characters without image exemplars.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
