DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents

• URL: http://arxiv.org/abs/2305.02208v1
• Date: Wed, 3 May 2023 15:45:30 GMT
• Title: DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
• Authors: Furkan Simsek, Brian Pfitzmann, Hendrik Raetz, Jona Otholt, Haojin Yang, Christoph Meinel
• Abstract summary: 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 We propose DocLangID, a transfer learning approach to identify the language of unlabeled historical documents.
• Score: 7.535751594024775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language identification describes the task of recognizing the language of written text in documents. This information is crucial because it can be used to support the analysis of a document's vocabulary and context. Supervised learning methods in recent years have advanced the task of language identification. However, these methods usually require large labeled datasets, which often need to be included for various domains of images, such as documents or scene images. In this work, we propose DocLangID, a transfer learning approach to identify the language of unlabeled historical documents. We achieve this by first leveraging labeled data from a different but related domain of historical documents.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unlabeled images, our feature extractor develops a better adaptability towards new and different data distributions of historical documents.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 Our experiments on historical documents demonstrate that our combined approach improves the language identification performance, achieving 74% recognition accuracy on the four unseen languages of the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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