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Communication dans un congrès Année : 2023

Applying Handwritten Text Recognition to the Distinctiones Collections

Résumé

Due to the development of neural network-based techniques, Handwritten Text Recognition is undergoing an unprecedented rise. A few years ago, the dominant approach was to create a unique model for each new project. Today, the more popular strategy is transfer learning followed by fine-tuning, made possible by initiatives such as HTR-united, CremmaLab, and others. The DISTINGUO project aims at the text extraction of the collections of distinctiones from scanned manuscripts and incunables and their publication in the form of a searchable database. Given the massive variety of hands and fonts in the corpus and limited human resources, we decided to use eScriptorium to create one general model, which is fine-tuned for each particular manuscript or book. However, this approach raises the question of how much data is needed so that the model could serve as a pre-trained base to raise a new specialized model. This presentation aims at suggesting the answer to this question and focusing on calculating the learning break point of the fine-tuned models, as well as on the post-processing techniques that can improve the quality of the transcribed text.

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Dates et versions

halshs-04460063, version 1 (15-02-2024)

Identifiants

  • HAL Id : halshs-04460063 , version 1

Citer

Svetlana Yatsyk. Applying Handwritten Text Recognition to the Distinctiones Collections: Building a Broad Model for Fine-Tuning with eScriptorium. International Medieval Congress, University of Leeds, Jul 2023, Leeds, United Kingdom. ⟨halshs-04460063⟩
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Dernière date de mise à jour le 12/05/2024
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