Papers › DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
Denis Coquenet, Clément Chatelain, Thierry Paquet
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Handwritten Text Recognition | READ 2016 | DAN | CER (%) | 3.22 | #1 of 2 | Archive leaderboard | report |
| Handwritten Text Recognition | READ 2016 | DAN | WER (%) | 13.63 | #1 of 2 | Archive leaderboard | report |
| Handwritten Text Recognition | READ2016(line-level) | DAN | Test CER | 4.1 | #3 of 5 | Archive leaderboard | report |
| Handwritten Text Recognition | READ2016(line-level) | DAN | Test WER | 17.6 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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