瑞士民众倡议中签名列表的手写提取与分析
Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives
AI总结：
针对瑞士民众倡议中签名列表验证的繁重人工流程，提出结合模板行分割、OCR和基于AI的手写分析（特别是作者检索）的自动化管道，实验表明OCR对短文本识别率低（CER 29.6%），而作者检索mAP达50.6%，可有效支持重复提交检测。
摘要：
民众倡议和公投是瑞士民主的核心，然而手写签名列表的验证仍然是一个劳动密集型的手工过程。本文研究了自动化文档分析方法的潜力，包括OCR和基于AI的手写分析，以支持这一任务。我们提出了一种结合基于模板的行分割与文本识别和作者检索技术的流水线，并在包含418位作者的443条手写条目的数据集上进行了评估。结果表明，OCR在处理词汇表外的手写文本时表现不佳，名字的词错误率（CER）为29.6%。相比之下，作者检索表现更为稳健，平均精度（mAP）达到50.6%。此外，我们的实验表明，现成的OCR系统对于手写签名数据的转录不够可靠，尤其是对于姓名或地址等短且词汇表外的条目。然而，作者检索方法可以有效地识别签名列表中视觉相似的条目，使其成为基于手写相似性支持检测潜在重复提交的合适工具。
英文摘要：
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.
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。
