Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts - Archive ouverte HAL Access content directly
Conference Papers Year :

Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts

(1, 2, 3, 4)
1
2
3
4

Abstract

As more and more projects openly release ground truth for handwritten text recognition (HTR), we expect the quality of automatic transcription to improve on unseen data. Getting models robust to scribal and material changes is a necessary step for specific data mining tasks. However, evaluation of HTR results requires ground truth to compare prediction statistically. In the context of modern languages, successful attempts to evaluate quality have been done using lexical features or n-grams.This, however, proves difficult in the context of spelling variation that both Old French and Latin have, even more so in the context of sometime heavily abbreviated manuscripts. We propose a new method based on deep learning where we attempt to categorize each line error rate into four error rate ranges (0 < 10% < 25% < 50% < 100%) using three different encoder (GRU with Attention, BiLSTM, TextCNN). To train these models, we propose a new dataset engineering approach using early stopped model, as an alternative to rule-based fake predictions. Our model largely outperforms the n-gram approach. We also provide an example application to qualitatively analyse our classifier, using classification on new prediction on a sample of 1,800 manuscripts ranging from the 9 th century to the 15 th .
Fichier principal
Vignette du fichier
CHR2022___State_of_HTR.pdf (5.68 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03828529 , version 1 (25-10-2022)

Identifiers

  • HAL Id : hal-03828529 , version 1

Cite

Thibault Clérice. Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts. Computational Humanities Research Conference (CHR) 2022, Dec 2022, Antwerp, Belgium. ⟨hal-03828529⟩
0 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More