Application of deep learning approaches for medieval historical documents transcription

• URL: http://arxiv.org/abs/2512.18865v1
• Date: Sun, 21 Dec 2025 19:43:30 GMT
• Title: Application of deep learning approaches for medieval historical documents transcription
• Authors: Maksym Voloshchuk, Bohdana Zarembovska, Mykola Kozlenko,
• Abstract summary: This paper presents a deep learning method to extract text information from handwritten Latin-language documents of the 9th to 11th centuries.<n>The approach takes into account the properties inherent in medieval documents.<n>The implementation is published on the GitHub repository.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 The paper provides a brief introduction to the field of historical document transcription, a first-sight analysis of the raw data, and the related works and studies.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.

Related papers

• Contextual Document Embeddings [77.22328616983417]
  We propose two complementary methods for contextualized document embeddings. First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss. Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
  arXiv  Detail & Related papers  (2024-10-03T14:33:34Z)
• Unifying Multimodal Retrieval via Document Screenshot Embedding [92.03571344075607]
  Document Screenshot Embedding (DSE) is a novel retrieval paradigm that regards document screenshots as a unified input format.<n>We first craft the dataset of Wiki-SS, a 1.3M Wikipedia web page screenshots as the corpus to answer the questions from the Natural Questions dataset.<n>For example, DSE outperforms BM25 by 17 points in top-1 retrieval accuracy. Additionally, in a mixed-modality task of slide retrieval, DSE significantly outperforms OCR text retrieval methods by over 15 points in nDCG@10.
  arXiv  Detail & Related papers  (2024-06-17T06:27:35Z)
• A Novel Dataset for Non-Destructive Inspection of Handwritten Documents [0.0]
  Forensic handwriting examination aims to examine handwritten documents in order to properly define or hypothesize the manuscript's author. We propose a new and challenging dataset consisting of two subsets: the first consists of 21 documents written either by the classic pen and paper" approach (and later digitized) and directly acquired on common devices such as tablets. Preliminary results on the proposed datasets show that 90% classification accuracy can be achieved on the first subset.
  arXiv  Detail & Related papers  (2024-01-09T09:25:58Z)
• Prompt me a Dataset: An investigation of text-image prompting for historical image dataset creation using foundation models [0.9065034043031668]
  We present a pipeline for image extraction from historical documents using foundation models. We evaluate text-image prompts and their effectiveness on humanities datasets of varying levels of complexity.
  arXiv  Detail & Related papers  (2023-09-04T15:37:03Z)
• DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents [7.535751594024775]
  Language identification describes the task of recognizing the language of written text in documents. We propose DocLangID, a transfer learning approach to identify the language of unlabeled historical documents.
  arXiv  Detail & Related papers  (2023-05-03T15:45:30Z)
• Augraphy: A Data Augmentation Library for Document Images [59.457999432618614]
  Augraphy is a Python library for constructing data augmentation pipelines. It provides strategies to produce augmented versions of clean document images that appear to have been altered by standard office operations.
  arXiv  Detail & Related papers  (2022-08-30T22:36:19Z)
• Unified Pretraining Framework for Document Understanding [52.224359498792836]
  We present UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understanding tasks, extending the Transformer to take multimodal embeddings as input. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses.
  arXiv  Detail & Related papers  (2022-04-22T21:47:04Z)
• Digital Peter: Dataset, Competition and Handwriting Recognition Methods [0.685068326729525]
  This paper presents a new dataset of Peter the Great's manuscripts. It consists of 9 694 images and text files corresponding to lines in historical documents.
  arXiv  Detail & Related papers  (2021-03-16T22:37:22Z)
• Handwriting Classification for the Analysis of Art-Historical Documents [6.918282834668529]
  We focus on the analysis of handwriting in scanned documents from the art-historic archive of the WPI. We propose a handwriting classification model that labels extracted text fragments based on their visual structure.
  arXiv  Detail & Related papers  (2020-11-04T13:06:46Z)
• Neural Deepfake Detection with Factual Structure of Text [78.30080218908849]
  We propose a graph-based model for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph. Our model can distinguish the difference in the factual structure between machine-generated text and human-written text.
  arXiv  Detail & Related papers  (2020-10-15T02:35:31Z)
• Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation [50.01708049531156]
  We focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference.
  arXiv  Detail & Related papers  (2020-02-24T12:52:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.