# The Effects of Character-Level Data Augmentation on Style-Based Dating of Historical Manuscripts

arXiv: https://arxiv.org/abs/2212.07923 · HTML: https://mlsanity.com/paper/2212.07923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks](https://mlsanity.com/paper/1911.07930/) (2019)

## Built on by (most cited first, 1 of 1)

- [Segmentation of Ink and Parchment in Dead Sea Scroll Fragments](https://mlsanity.com/paper/2411.10668/) (2024)

## Abstract (arXiv)

Identifying the production dates of historical manuscripts is one of the main goals for paleographers when studying ancient documents. Automatized methods can provide paleographers with objective tools to estimate dates more accurately. Previously, statistical features have been used to date digitized historical manuscripts based on the hypothesis that handwriting styles change over periods. However, the sparse availability of such documents poses a challenge in obtaining robust systems. Hence, the research of this article explores the influence of data augmentation on the dating of historical manuscripts. Linear Support Vector Machines were trained with k-fold cross-validation on textural and grapheme-based features extracted from historical manuscripts of different collections, including the Medieval Paleographical Scale, early Aramaic manuscripts, and the Dead Sea Scrolls. Results show that training models with augmented data improve the performance of historical manuscripts dating by 1% - 3% in cumulative scores. Additionally, this indicates further enhancement possibilities by considering models specific to the features and the documents' scripts.