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|a 10.1109/TPAMI.2022.3144899
|2 doi
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|a eng
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| 100 |
1 |
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|a Coquenet, Denis
|e verfasserin
|4 aut
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| 245 |
1 |
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|a End-to-End Handwritten Paragraph Text Recognition Using a Vertical Attention Network
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| 264 |
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 05.04.2023
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|a Date Revised 05.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Unconstrained handwritten text recognition remains challenging for computer vision systems. Paragraph text recognition is traditionally achieved by two models: the first one for line segmentation and the second one for text line recognition. We propose a unified end-to-end model using hybrid attention to tackle this task. This model is designed to iteratively process a paragraph image line by line. It can be split into three modules. An encoder generates feature maps from the whole paragraph image. Then, an attention module recurrently generates a vertical weighted mask enabling to focus on the current text line features. This way, it performs a kind of implicit line segmentation. For each text line features, a decoder module recognizes the character sequence associated, leading to the recognition of a whole paragraph. We achieve state-of-the-art character error rate at paragraph level on three popular datasets: 1.91% for RIMES, 4.45% for IAM and 3.59% for READ 2016. Our code and trained model weights are available at https://github.com/FactoDeepLearning/VerticalAttentionOCR
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Chatelain, Clement
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Paquet, Thierry
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 1 vom: 02. Jan., Seite 508-524
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:45
|g year:2023
|g number:1
|g day:02
|g month:01
|g pages:508-524
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|u http://dx.doi.org/10.1109/TPAMI.2022.3144899
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