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approximate_ground_truth_generation_for_semantic_2024
Journal article

Approximate ground truth generation for semantic labeling of historical documents with minimal human effort

BP2-STS

  • 2024
Published in:
  • International Journal on Document Analysis and Recognition (IJDAR). - Springer Science and Business Media LLC. - 2024, vol. 27, p. 335-347
Historical documents
Layout analysis
Deep learning
Synthetic data
English Deep learning approaches have shown high performance for layout analysis of historical documents, provided that enough labeled data is available. This is not an issue for generic tasks such as image binarization, text graphics separation, or text line and text block detection but can become an impediment for more specialized tasks specific to one or a few books only. This paper addresses layout analysis of medieval books with rich and complex layouts, for which no labeled data is initially available. The proposed strategy consists of training an initial model with artificial data created to reflect the rules a deep neural network should learn. Then, the model is iteratively fine-tuned by mixing the artificial data with real data obtained by previous predictions, post-processed, and manually selected by an expert user. Such a strategy needs less human effort than manual ground truthing. The approach is qualitatively and quantitatively assessed and shows that the system converges to an accurate model that finally produces approximate ground truth stable and good enough to train a final model to solve the targeted task with high accuracy.
Faculty
Faculté des sciences et de médecine
Department
Département d'Informatique
Language
  • English
Classification
Medicine, Technology, Engineering
License
CC BY
Open access status
hybrid
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/334263
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