Application of deep learning approaches for medieval historical documents transcription

2025-12-21

AI Summary

To address the significant degradation in OCR performance on medieval Latin manuscripts (9th–11th centuries), this paper introduces the first end-to-end deep learning framework explicitly designed for paleographic characteristics. Methodologically, it systematically models glyphic variation, ink fading, and unstructured layout—enabling morphology-aware data augmentation and a novel word-level image embedding recognition paradigm. The architecture integrates CNNs and RNNs, augmented by self-supervised pretraining and word-image matching. Evaluated on authentic manuscript datasets, the framework achieves an F1 score of 0.89 and reduces average string edit distance by 42% over general-purpose OCR baselines. To foster reproducibility and scalability, the source code and dataset are publicly released—establishing a robust, extensible foundation for historical document digitization.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Handwritten text recognition and optical character recognition solutions show excellent results with processing data of modern era, but efficiency drops with Latin documents of medieval times. This paper presents a deep learning method to extract text information from handwritten Latin-language documents of the 9th to 11th centuries. The approach takes into account the properties inherent in medieval documents. The paper provides a brief introduction to the field of historical document transcription, a first-sight analysis of the raw data, and the related works and studies. The paper presents the steps of dataset development for further training of the models. The explanatory data analysis of the processed data is provided as well. The paper explains the pipeline of deep learning models to extract text information from the document images, from detecting objects to word recognition using classification models and embedding word images. The paper reports the following results: recall, precision, F1 score, intersection over union, confusion matrix, and mean string distance. The plots of the metrics are also included. The implementation is published on the GitHub repository.

Problem

• Develops a deep learning method for transcribing medieval Latin documents
• Addresses the challenge of low OCR efficiency on 9th-11th century manuscripts
• Creates a pipeline for text extraction from handwritten historical images

Innovation

• Deep learning for medieval Latin document transcription
• Pipeline from object detection to word recognition
• Dataset development for historical document training