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Handwritten text recognition has been developed rapidly in the recent years, following the rise of deep learning and its applications.
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Fischer, A.: Handwriting recognition in historical documents. Ph.D. thesis, Verlag nicht ermittelbar (2012)
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Graves, A.: Connectionist temporal classification. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 61–93. Springer (2012)
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
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Leifert, G., Strau, T., Gr, T., Wustlich, W., Labahn, R., et al.: Cells in multidimensional recurrent neural networks. Journal of Machine Learning Research 17
2016
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Sudholt, S., Fink, G.A.: PHOCNet: A deep convolutional neural network for word spotting in handwritten documents. In: Proceedings of the 15 t h 15^{th} International Conference on Frontiers in Handwriting Recognition (ICFHR). pp. 277–282 (2016)
2016
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Chen, Z., Wu, Y., Yin, F., Liu, C.L.: Simultaneous script identification and handwriting recognition via multi-task learning of recurrent neural networks. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). vol. 1, pp. 525–530. IEEE (2017)
2017
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Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). vol. 1, pp. 67–72. IEEE (2017)
2017
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Michael, J., Labahn, R., Grüning, T., Zöllner, J.: Evaluating sequence-to-sequence models for handwritten text recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1286–1293. IEEE (2019)
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Luo, C., Zhu, Y., Jin, L., Wang, Y.: Learn to augment: Joint data augmentation and network optimization for text recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13746–13755 (2020)
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Markou, K., Tsochatzidis, L., Zagoris, K., Papazoglou, A., Karagiannis, X., Symeonidis, S., Pratikakis, I.: A convolutional recurrent neural network for the handwritten text recognition of historical greek manuscripts. In: International Workshop on Pattern Recognition for Cultural Heritage (PATRECH) (2020)
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Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Cited alongside, same era.
Wigington, C., Stewart, S., Davis, B., Barrett, B., Price, B., Cohen, S.: Data augmentation for recognition of handwritten words and lines using a cnn-lstm network. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). vol. 1, pp. 639–645. IEEE (2017)
2017
Cited alongside, same era.
Chowdhury, A., Vig, L.: An efficient end-to-end neural model for handwritten text recognition (2018)
2018
Cited alongside, same era.
Dutta, K., Krishnan, P., Mathew, M., Jawahar, C.: Improving CNN-RNN hybrid networks for handwriting recognition. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). pp. 80–85. IEEE (2018)
2018
Cited alongside, same era.
Krishnan, P., Dutta, K., Jawahar, C.: Word spotting and recognition using deep embedding. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). pp. 1–6. IEEE (2018)
2018
Cited alongside, same era.
Retsinas, G., Sfikas, G., Gatos, B.: Transferable deep features for keyword spotting. In: Multidisciplinary Digital Publishing Institute Proceedings. vol. 2, p. 89 (2018)
2018
Cited alongside, same era.
Sueiras, J., Ruiz, V., Sanchez, A., Velez, J.F.: Offline continuous handwriting recognition using sequence to sequence neural networks. Neurocomputing 289
2018
Cited alongside, same era.
2020
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Yousef, M., Hussain, K.F., Mohammed, U.S.: Accurate, data-efficient, unconstrained text recognition with convolutional neural networks. Pattern Recognition 108
2020
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Retsinas, G., Sfikas, G., Nikou, C.: Iterative weighted transductive learning for handwriting recognition. In: International Conference on Document Analysis and Recognition. pp. 587–601. Springer (2021)
2021
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Retsinas, G., Sfikas, G., Nikou, C., Maragos, P.: Deformation-invariant networks for handwritten text recognition. In: 2021 IEEE International Conference on Image Processing (ICIP). pp. 949–953. IEEE (2021)
2021
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Retsinas, G., Sfikas, G., Nikou, C., Maragos, P.: From Seq2Seq recognition to handwritten word embeddings. In: Proceedings of the British Machine Vision Conference (BMVC) (2021)
2021
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Tassopoulou, V., Retsinas, G., Maragos, P.: Enhancing handwritten text recognition with n-gram sequence decomposition and multitask learning. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 10555–10560. IEEE (2021)
2021
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Wick, C., Zöllner, J., Grüning, T.: Transformer for handwritten text recognition using bidirectional post-decoding. In: International Conference on Document Analysis and Recognition. pp. 112–126. Springer (2021)
2021
Later among the works it cites.