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AttentionHTR :
AttentionHTR : Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
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- Kass, Dmitrijs (författare)
- Uppsala universitet,Institutionen för informationsteknologi
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- Vats, Ekta (författare)
- Uppsala universitet,Institutionen för ABM,Ctr Digital Humanities Uppsala
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(creator_code:org_t)
- 2022-05-18
- 2022
- Engelska.
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Ingår i: DOCUMENT ANALYSIS SYSTEMS, DAS 2022. - Cham : Springer Nature. - 9783031065552 - 9783031065545 ; , s. 507-522
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. ResNet feature extraction and bidirectional LSTM-based sequence modeling stages together form an encoder. The prediction stage consists of a decoder and a content-based attention mechanism. The effectiveness of the proposed end-to-end HTR system has been empirically evaluated on a novel multi-writer dataset Imgur5K and the IAM dataset. The experimental results evaluate the performance of the HTR framework, further supported by an in-depth analysis of the error cases. Source code and pre-trained models are available at GitHub (https://github.com/dmitrijsk/AttentionHTR).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Handwritten text recognition
- Attention encoder-decoder networks
- Sequence-to-sequence model
- Transfer learning
- Multi-writer
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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