AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks

Bookstein, F.L.: Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on pattern analysis and machine intelligence 11

1989

Earlier work this paper cites.

Marti, U.V., Bunke, H.: The iam-database: an english sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5

2002

Earlier work this paper cites.

Frinken, V., Fischer, A., Manmatha, R., Bunke, H.: A novel word spotting method based on recurrent neural networks. IEEE transactions on pattern analysis and machine intelligence 34

2012

Earlier work this paper cites.

Rodríguez-Serrano, J.A., Perronnin, F., et al.: A model-based sequence similarity with application to handwritten word spotting. IEEE transactions on pattern analysis and machine intelligence 34

2012

Earlier work this paper cites.

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

Original

2014

Earlier work this paper cites.

Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:1406.2227 (2014)

Original

2014

Earlier work this paper cites.

Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: IEEE conference on computer vision and pattern recognition. pp. 2315–2324 (2016)

2016

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

2016

Cited alongside, same era.

Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence 39

2016

Cited alongside, same era.

Bluche, T., Louradour, J., Messina, R.: Scan, attend and read: End-to-end handwritten paragraph recognition with mdlstm attention. In: 14th IAPR international conference on document analysis and recognition. vol. 1, pp. 1050–1055 (2017)

2017

Cited alongside, same era.

Chowdhury, A., Vig, L.: An efficient end-to-end neural model for handwritten text recognition. arXiv preprint arXiv:1807.07965 (2018)

Original

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

Later among the works it cites.

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: International conference on artificial neural networks. pp. 270–279. Springer (2018)

2018

Later among the works it cites.

Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., Oh, S.J., Lee, H.: What is wrong with scene text recognition model comparisons? dataset and model analysis. In: IEEE International Conference on Computer Vision. pp. 4715–4723 (2019)

2019

Later among the works it cites.

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)

2019

Later among the works it cites.