# Cross-codex Learning for Reliable Scribe Identification in Medieval Manuscripts

arXiv: https://arxiv.org/abs/2312.04296 · HTML: https://mlsanity.com/paper/2312.04296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/1707.02131/) (2017)
- [A Survey on Optical Character Recognition System](https://mlsanity.com/paper/1710.05703/) (2017)
- [DeepWriterID: An End-to-end Online Text-independent Writer Identification System](https://mlsanity.com/paper/1508.04945/) (2015)
- [DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification](https://mlsanity.com/paper/1606.06472/) (2016)
- [ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents](https://mlsanity.com/paper/1912.03713/) (2019)
- [A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis](https://mlsanity.com/paper/1905.09113/) (2019)
- [Handwriting Recognition of Historical Documents with few labeled data](https://mlsanity.com/paper/1811.07768/) (2018)
- [Attention based Writer Independent Handwriting Verification](https://mlsanity.com/paper/2009.04532/) (2020)
- [PageNet: Page Boundary Extraction in Historical Handwritten Documents](https://mlsanity.com/paper/1709.01618/) (2017)

## Abstract (arXiv)

Historic scribe identification is a substantial task for obtaining information about the past. Uniform script styles, such as the Carolingian minuscule, make it a difficult task for classification to focus on meaningful features. Therefore, we demonstrate in this paper the importance of cross-codex training data for CNN based text-independent off-line scribe identification, to overcome codex dependent overfitting. We report three main findings: First, we found that preprocessing with masked grayscale images instead of RGB images clearly increased the F1-score of the classification results. Second, we trained different neural networks on our complex data, validating time and accuracy differences in order to define the most reliable network architecture. With AlexNet, the network with the best trade-off between F1-score and time, we achieved for individual classes F1-scores of up to 0,96 on line level and up to 1.0 on page level in classification. Third, we could replicate the finding that the CNN output can be further improved by implementing a reject option, giving more stable results. We present the results on our large scale open source dataset -- the Codex Claustroneoburgensis database (CCl-DB) -- containing a significant number of writings from different scribes in several codices. We demonstrate for the first time on a dataset with such a variety of codices that paleographic decisions can be reproduced automatically and precisely with CNNs. This gives manifold new and fast possibilities for paleographers to gain insights into unlabeled material, but also to develop further hypotheses.