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Recognizing text lines from images is a challenging problem, especially for handwritten documents due to large variations in writing styles.
The IAM-database: an English sentence database for offline handwriting recognition
Marti, U.-V.; and Bunke, H. 2002 · 2002
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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Graves, A.; Fernández, S.; Gomez, F.; and Schmidhuber, J. 2006 · 2006
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Evaluating prediction-time batch normalization for robustness under covariate shift
Nado, Z.; Padhy, S.; Sculley, D.; D’Amour, A.; Lakshminarayanan, B.; and Snoek, J. 2020 · 2006
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High-performance OCR for printed English and Fraktur using LSTM networks
Breuel, T. M.; Ul-Hasan, A.; Al-Azawi, M. A.; and Shafait, F. 2013 · 2013
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Cvl-database: An off-line database for writer retrieval, writer identification and word spotting
Kleber, F.; Fiel, S.; Diem, M.; and Sablatnig, R. 2013 · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A.; Yosinski, J.; and Clune, J. 2015 · 2015
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ICDAR 2015 competition HTRtS: Handwritten Text Recognition on the tranScriptorium dataset
Sanchez, J. A.; Toselli, A. H.; Romero, V.; and Vidal, E. 2015 · 2015
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; and Adam, H. 2017 · 2017
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Vaswani, A.; Shazeer, N. M.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J.; Tzeng, E.; Park, T.; Zhu, J.-Y.; Isola, P.; Saenko, K.; Efros, A.; and Darrell, T. 2018 · 2018
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Learning to adapt structured output space for semantic segmentation
Tsai, Y.-H.; Hung, W.-C.; Schulter, S.; Sohn, K.; Yang, M.-H.; and Chandraker, M. 2018 · 2018
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Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning
Bhunia, A. K.; Das, A.; Bhunia, A. K.; Kishore, P. S. R.; and Roy, P. P. 2019 · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D.; and Dietterich, T. 2019 · 2019
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A scalable handwritten text recognition system
Ingle, R. R.; Fujii, Y.; Deselaers, T.; Baccash, J.; and Popat, A. C. 2019 · 2019
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Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition
Zhang, Y.; Nie, S.; Liu, W.; Xu, X.; Zhang, D.; and Shen, H. T. 2019 · 2019
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ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Fogel, S.; Averbuch-Elor, H.; Cohen, S.; Mazor, S.; and Litman, R. 2020 · 2020
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Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Kang, L.; Rusiñol, M.; Fornés, A.; Riba, P.; and Villegas, M. 2020 · 2020
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Improving Robustness against Common Corruptions by Covariate Shift Adaptation
Tent: Fully Test-Time Adaptation by Entropy Minimization
Wang, D.; Shelhamer, E.; Liu, S.; Olshausen, B.; and Darrell, T. 2021 · 2021
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MEMO: Test Time Robustness via Adaptation and Augmentation
Zhang, M.; Levine, S.; and Finn, C. 2021 · 2021
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Mt3: Meta test-time training for self-supervised test-time adaption
Bartler, A.; Bühler, A.; Wiewel, F.; Döbler, M.; and Yang, B. 2022 · 2022
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Contrastive test-time adaptation
Chen, D.; Wang, D.; Darrell, T.; and Ebrahimi, S. 2022 · 2022
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Scaling instruction-finetuned language models
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, E.; Wang, X.; Dehghani, M.; Brahma, S.; et al. 2022 · 2022
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Test-time adaptation via conjugate pseudo-labels
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Schneider, S.; Rusak, E.; Eck, L.; Bringmann, O.; Brendel, W.; and Bethge, M. 2020 · 2020
Cited alongside, same era.
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
Bhunia, A.; Ghose, S.; Kumar, A.; Chowdhury, P.; Sain, A.; and Song, Y. 2021 · 2021
Cited alongside, same era.
Rethinking Text Line Recognition Models
Diaz, D. H.; Qin, S.; Ingle, R. R.; Fujii, Y.; and Bissacco, A. 2021 · 2021
Cited alongside, same era.
Mixnorm: Test-time adaptation through online normalization estimation
Hu, X.; Uzunbas, G.; Chen, S.; Wang, R.; Shah, A.; Nevatia, R.; and Lim, S.-N. 2021 · 2021
Cited alongside, same era.
Sita: Single image test-time adaptation
Khurana, A.; Paul, S.; Rai, P.; Biswas, S.; and Aggarwal, G. 2021 · 2021
Cited alongside, same era.
GNHK: A Dataset for English Handwriting in the Wild
Lee, A. W. C.; Chung, J.; and Lee, M. 2021 · 2021
Cited alongside, same era.
Trocr: Transformer-based optical character recognition with pre-trained models
Li, M.; Lv, T.; Chen, J.; Cui, L.; Lu, Y.; Florencio, D.; Zhang, C.; Li, Z.; and Wei, F. 2021 · 2021
Cited alongside, same era.
Goyal, S.; Sun, M.; Raghunathan, A.; and Kolter, Z. 2022 · 2022
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Test-time adaptation with slot-centric models
Prabhudesai, M.; Paul, S.; van Steenkiste, S.; Sajjadi, M. S.; Goyal, A.; Pathak, D.; Fragkiadaki, K.; Aggarwal, G.; and Kipf, T. 2022 · 2022
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MM-TTA: multi-modal test-time adaptation for 3d semantic segmentation
Shin, I.; Tsai, Y.-H.; Zhuang, B.; Schulter, S.; Liu, B.; Garg, S.; Kweon, I. S.; and Yoon, K.-J. 2022 · 2022
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Domain Adaptation via Mutual Information Maximization for Handwriting Recognition
Tang, P.; Peng, L.; Yan, R.; Shi, H.; Yao, G.; Liu, C.; Li, J.; and Zhang, Y. 2022 · 2022
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KOHTD: Kazakh offline handwritten text dataset
Toiganbayeva, N.; Kasem, M.; Abdimanap, G.; Bostanbekov, K.; Abdallah, A.; Alimova, A.; and Nurseitov, D. 2022 · 2022
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Reconstructing training data from diverse ml models by ensemble inversion
Wang, Q.; and Kurz, D. 2022 · 2022
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Fast writer adaptation with style extractor network for handwritten text recognition
Wang, Z.-R.; and Du, J. 2022 · 2022
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Towards Writing Style Adaptation in Handwriting Recognition
Kohút, J.; Hradiš, M.; and Kišš, M. 2023 · 2023
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