# Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering

arXiv: https://arxiv.org/abs/2210.16780 · HTML: https://mlsanity.com/paper/2210.16780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [A Two-Stage Method for Text Line Detection in Historical Documents](https://mlsanity.com/paper/1802.03345/) (2018)
- [Unsupervised Feature Learning for Writer Identification and Writer Retrieval](https://mlsanity.com/paper/1705.09369/) (2017)
- [FragNet: Writer Identification using Deep Fragment Networks](https://mlsanity.com/paper/2003.07212/) (2020)
- [Fully Convolutional Networks for Handwriting Recognition](https://mlsanity.com/paper/1907.04888/) (2019)
- [Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification](https://mlsanity.com/paper/1901.03398/) (2019)
- [Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network](https://mlsanity.com/paper/1801.00470/) (2018)
- [Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs](https://mlsanity.com/paper/2101.09461/) (2021)
- [Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease](https://mlsanity.com/paper/1903.08226/) (2019)
- [An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting](https://mlsanity.com/paper/1708.03361/) (2017)

## Abstract (arXiv)

The forensic attribution of the handwriting in a digitized document to multiple scribes is a challenging problem of high dimensionality. Unique handwriting styles may be dissimilar in a blend of several factors including character size, stroke width, loops, ductus, slant angles, and cursive ligatures. Previous work on labeled data with Hidden Markov models, support vector machines, and semi-supervised recurrent neural networks have provided moderate to high success. In this study, we successfully detect hand shifts in a historical manuscript through fuzzy soft clustering in combination with linear principal component analysis. This advance demonstrates the successful deployment of unsupervised methods for writer attribution of historical documents and forensic document analysis.