DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
Authors: 
Furkan Simsek, Brian Pfitzmann, Hendrik Raetz, Jona Otholt, Haojin Yang, Christoph MeinelAuthors Info & Claims
Abstract
In this work, we propose DocLangID, a transfer learning approach to identify the language of unlabeled historical documents. We achieve this by first leveraging labeled data from a different but related domain of historical documents. Secondly, we implement a distance-based few-shot learning approach to adapt a convolutional neural network to new languages of the unlabeled dataset. By introducing small amounts of manually labeled examples from the set of unlabeled images, our feature extractor develops a better adaptability towards new and different data distributions of historical documents. We show that such a model can be effectively fine-tuned for the unlabeled set of images by only reusing the same few-shot examples. We showcase our work across 10 languages that mostly use the Latin script. Our experiments on historical documents demonstrate that our combined approach improves the language identification performance, achieving 74% recognition accuracy on the four unseen languages of the unlabeled dataset.
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Index Terms
- DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
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Published In
August 2023
117 pages
ISBN:9798400708411
DOI:10.1145/3604951
Copyright © 2023 ACM.
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Published: 25 August 2023
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HIP '23
HIP '23: 7th International Workshop on Historical Document Imaging and Processing
August 25 - 26, 2023
CA, San Jose, USA
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Overall Acceptance Rate 52 of 90 submissions, 58%
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