| Autor: |
Coquenet, Denis, Chatelain, Clément, Paquet, Thierry |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Pattern Analysis and Machine Intelligence 2023 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TPAMI.2023.3235826 |
| Popis: |
Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network. In addition to text recognition, the model is trained to label text parts using begin and end tags in an XML-like fashion. This model is made up of an FCN encoder for feature extraction and a stack of transformer decoder layers for a recurrent token-by-token prediction process. It takes whole text documents as input and sequentially outputs characters, as well as logical layout tokens. Contrary to the existing segmentation-based approaches, the model is trained without using any segmentation label. We achieve competitive results on the READ 2016 dataset at page level, as well as double-page level with a CER of 3.43% and 3.70%, respectively. We also provide results for the RIMES 2009 dataset at page level, reaching 4.54% of CER. We provide all source code and pre-trained model weights at https://github.com/FactoDeepLearning/DAN. |
| Databáze: |
arXiv |
| Externí odkaz: |
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