# Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives

arXiv: https://arxiv.org/abs/2606.05018 · HTML: https://mlsanity.com/paper/2606.05018/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 15 of 15)

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- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [Unsupervised Feature Learning for Writer Identification and Writer Retrieval](https://mlsanity.com/paper/1705.09369/) (2017)
- [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025)
- [Benchmarking Large Language Models for Handwritten Text Recognition](https://mlsanity.com/paper/2503.15195/) (2025)
- [Towards Writer Retrieval for Historical Datasets](https://mlsanity.com/paper/2305.05358/) (2023)
- [SAGHOG: Self-Supervised Autoencoder for Generating HOG Features for Writer Retrieval](https://mlsanity.com/paper/2404.17221/) (2024)
- [Self-Supervised Vision Transformers for Writer Retrieval](https://mlsanity.com/paper/2409.00751/) (2024)
- [Towards the Influence of Text Quantity on Writer Retrieval](https://mlsanity.com/paper/2506.07566/) (2025)

## Abstract (arXiv)

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.