# A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition

arXiv: https://arxiv.org/abs/2009.12577 · HTML: https://mlsanity.com/paper/2009.12577/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents](https://mlsanity.com/paper/2112.04189/) (2021)
- [Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2107.10064/) (2021)
- [Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement](https://mlsanity.com/paper/2105.12710/) (2021)
- [One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2105.05300/) (2021)
- [The Learnable Typewriter: A Generative Approach to Text Analysis](https://mlsanity.com/paper/2302.01660/) (2023)
- [A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts](https://mlsanity.com/paper/2209.10441/) (2022)
- [VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts](https://mlsanity.com/paper/2304.00746/) (2023)
- [Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach](https://mlsanity.com/paper/2411.16408/) (2024)
- [Handwritten Stenography Recognition and the LION Dataset](https://mlsanity.com/paper/2308.07799/) (2023)
- [Unsupervised Domain Adaptation for Symbol Spotting in Historical Encrypted Manuscripts](https://mlsanity.com/paper/2609.07159/) (2026)

## Abstract (arXiv)

Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.