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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.06308 |
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| _version_ | 1866912353943027712 |
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| author | Kohút, Jan Hradiš, Michal |
| author_facet | Kohút, Jan Hradiš, Michal |
| contents | In many machine learning tasks, a large general dataset and a small specialized dataset are available. In such situations, various domain adaptation methods can be used to adapt a general model to the target dataset. We show that in the case of neural networks trained for handwriting recognition using CTC, simple fine-tuning with data augmentation works surprisingly well in such scenarios and that it is resistant to overfitting even for very small target domain datasets. We evaluated the behavior of fine-tuning with respect to augmentation, training data size, and quality of the pre-trained network, both in writer-dependent and writer-independent settings. On a large real-world dataset, fine-tuning on new writers provided an average relative CER improvement of 25 % for 16 text lines and 50 % for 256 text lines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_06308 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fine-tuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition Kohút, Jan Hradiš, Michal Computer Vision and Pattern Recognition In many machine learning tasks, a large general dataset and a small specialized dataset are available. In such situations, various domain adaptation methods can be used to adapt a general model to the target dataset. We show that in the case of neural networks trained for handwriting recognition using CTC, simple fine-tuning with data augmentation works surprisingly well in such scenarios and that it is resistant to overfitting even for very small target domain datasets. We evaluated the behavior of fine-tuning with respect to augmentation, training data size, and quality of the pre-trained network, both in writer-dependent and writer-independent settings. On a large real-world dataset, fine-tuning on new writers provided an average relative CER improvement of 25 % for 16 text lines and 50 % for 256 text lines. |
| title | Fine-tuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2302.06308 |