Application of deep learning approaches for medieval historical documents transcription

TL;DR

This paper develops a deep learning pipeline tailored for transcribing medieval Latin handwritten documents from the 9th to 11th centuries, addressing challenges posed by historical handwriting styles.

Contribution

It introduces a specialized dataset, analysis, and a comprehensive deep learning approach for medieval document transcription, filling a gap in historical handwriting recognition.

Findings

Achieved high precision and recall metrics

Developed a dataset for medieval Latin scripts

Published the implementation on GitHub

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 The paper provides a brief introduction to the field of historical document transcription, a first-sight analysis of the raw data, and the related works and studies.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.