用写字风格识别自动检测瑞士签名重复，提升民主流程效率
Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives
- 结合模板分割与手写识别，构建签名列表自动化分析流水线
- 手写者检索准确率达50.6% mAP，优于传统OCR的29.6%字符错误率
- 适合需要筛查签名重复的政府或选举机构使用
瑞士全民公投中的签名验证依赖人工，耗时费力。本文研究基于OCR和人工智能的手写分析方法在该任务中的应用潜力。提出一种结合模板化行分割、文本识别与作者检索的处理流程，在418位作者的443份手写签名数据上进行评估。结果显示，传统OCR在未登录词汇手写体上表现不佳，名字识别字符错误率达29.6%；而作者检索方法更为稳健，平均精度达50.6% mAP。实验表明，现成OCR系统难以可靠转录短且非标准的签名内容（如姓名、地址），但作者检索可有效识别跨列表中视觉相似的签名，适用于检测潜在重复提交。
原文摘要 · Abstract (English)
Popular initiatives and referendums are central to Swiss democracy, yet the validation of handwritten signature lists remains a labor-intensive manual process. This paper investigates the potential of automated document analysis methods, including OCR and AI-based handwriting analysis, to support this task. We propose a pipeline combining template-based line segmentation with text recognition and writer retrieval techniques, evaluated on a dataset of 443 handwritten entries from 418 writers. Results show that OCR struggles with out-of-vocabulary handwriting, with a CER of 29.6% for first names. In contrast, writer retrieval performs more robustly, reaching an mAP of 50.6%. Furthermore, our experiments indicate that off-the-shelf OCR systems are not sufficiently reliable for transcription of handwritten signature data, particularly for short, out-of-vocabulary entries such as names or addresses. However, writer retrieval methods can effectively identify visually similar entries across signature lists, making them a suitable tool for supporting the detection of potential duplicate submissions based on handwriting similarity.
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