[1] “Cross lingual handwritten character recognition using long short term memory network with aid of elephant herding optimization algorithm,” Pattern Recognit Lett, vol. 159, pp. 16–22, Jul. 2022, doi: 10.1016/J.PATREC.2022.04.038.
[2] J. Almazan, A. Gordo, A. Fornes, and E. Valveny, “Word spotting and recognition with embedded attributes,” IEEE Trans Pattern Anal Mach Intell, vol. 36, no. 12, pp. 2552–2566, Dec. 2014, doi: 10.1109/TPAMI.2014.2339814.
[3] L. Kang, P. Riba, M. Rusiñol, A. Fornés, and M. Villegas, “Distilling Content from Style for Handwritten Word Recognition.” 17th International Conference on Frontiers 2020.
[4] R. Bhattacharya, S. Malakar, F. Schwenker, and R. Sarkar, “Fuzzy-based pseudo segmentation approach for handwritten word recognition using a sequence to sequence model with attention,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12662 LNCS, pp. 582–596, 2021, doi: 10.1007/978-3-030-68790-8_45.
[5] S. España-Boquera, M. J. Castro-Bleda, J. Gorbe-Moya, and F. Zamora-Martinez, “Improving offline handwritten text recognition with hybrid HMM/ANN models,” IEEE Trans Pattern Anal Mach Intell, vol. 33, no. 4, pp. 767–779, 2011, doi: 10.1109/TPAMI.2010.141.
[6] R. Mondal, S. Malakar, E. H. Barney Smith, and R. Sarkar, “Handwritten English word recognition using a deep learning based object detection architecture,” Multimed Tools Appl, vol. 81, no. 1, pp. 975–1000, Jan. 2022, doi: 10.1007/s11042-021-11425-7.
[7] T. Bluche, H. Ney, and C. Kermorvant, “A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8791, pp. 199–219, 2014, doi: 10.1007/978-3-319-11397-5_15/COVER.
[8] D. Kass and E. Vats, “AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks,” Jan. 2022, [Online]. Available: http://arxiv.org/abs/2201.09390
[9] P. Doetsch, M. Kozielski, and H. Ney, “Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition,” Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, vol. 2014-December, pp. 279–284, Dec. 2014, doi: 10.1109/ICFHR.2014.54.
[10] M. Ashurpour and M. Ziaratban, “بازشناسی برخط حروف مجزای دست نویس فارسی با استفاده از مدل مخفی مارکوف گسسته و ویژگیهای مبتنی بر زبان فارسی,” مجله علمی رایانش نرم و فناوری اطلاعات, vol. 6, no. 2, pp. 51–68, Sep. 2017, Accessed: Feb. 20, 2023. [Online]. Available: https://jscit.nit.ac.ir/article_61690.html
[11] M. Yousef, K. F. Hussain, and U. S. Mohammed, “Accurate, data-efficient, unconstrained text recognition with convolutional neural networks,” Pattern Recognit, vol. 108, Dec. 2020, doi: 10.1016/j.patcog.2020.107482.
[12] A. A. A. Ali and S. Mallaiah, “Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 3294–3300, Jun. 2022, doi: 10.1016/J.JKSUCI.2021.01.012.
[13] Z. Ullah and M. Jamjoom, “An intelligent approach for Arabic handwritten letter recognition using convolutional neural network,” PeerJ Comput Sci, vol. 8, p. e995, May 2022, doi: 10.7717/PEERJ-CS.995/SUPP-2.
[14] P. Lopez-Rodriguez, J. G. Avina-Cervantes, J. L. Contreras-Hernandez, R. Correa, and J. Ruiz-Pinales, “Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning,” Applied Sciences 2022, Vol. 12, Page 6707, vol. 12, no. 13, p. 6707, Jul. 2022, doi: 10.3390/APP12136707.
[15] V. Carbune et al., “Fast multi-language LSTM-based online handwriting recognition,” International Journal on Document Analysis and Recognition, vol. 23, no. 2, pp. 89–102, Jun. 2020, doi: 10.1007/S10032-020-00350-4/TABLES/9.
[16] K. Dutta, P. Krishnan, M. Mathew, and C. V. Jawahar, “Improving CNN-RNN hybrid networks for handwriting recognition,” Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, vol. 2018-August, pp. 80–85, Dec. 2018, doi: 10.1109/ICFHR-2018.2018.00023.
[17] J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” 2015.
[18] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,” pp. 1–12, Dec. 2014, doi: 10.48550/arxiv.1412.7062.
[19] P. Y. Simard, J. C. Platt, and D. Steinkraus, “Best Practices for Convolutional Neural Networks Streaming data View project Machine Teaching View project Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis,” 2003, Accessed: Sep. 16, 2022. [Online]. Available: https://www.researchgate.net/publication/2880624
[20] M. Jaderberg, K. Simonyan, A. Zisserman, and koray kavukcuoglu, “Spatial Transformer Networks,” Adv Neural Inf Process Syst, vol. 28, 2015.
[21] D. X. Zhou, “Universality of deep convolutional neural networks,” Appl Comput Harmon Anal, vol. 48, no. 2, pp. 787–794, Mar. 2020, doi: 10.1016/J.ACHA.2019.06.004.
[22] Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language Modeling with Gated Convolutional Networks,” Jul. 17, 2017, PMLR. Accessed: Sep. 16, 2022. [Online]. Available: https://proceedings.mlr.press/v70/dauphin17a.html
[23] Y. N. Dauphin and D. Grangier, “Predicting distributions with Linearizing Belief Networks,” 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, Nov. 2015, doi: 10.48550/arxiv.1511.05622.
[24] M. M. Misgar, F. Mushtaq, S. S. Khurana, and M. Kumar, “Recognition of offline handwritten Urdu characters using RNN and LSTM models,” Multimedia Tools and Applications 2022, pp. 1–24, Jun. 2022, doi: 10.1007/S11042-022-13320-1.
[25] Z. Chen, F. Yin, X. Y. Zhang, Q. Yang, and C. L. Liu, “MuLTReNets: Multilingual text recognition networks for simultaneous script identification and handwriting recognition,” Pattern Recognit, vol. 108, p. 107555, Dec. 2020, doi: 10.1016/J.PATCOG.2020.107555.
[26] A. Abdallah, M. Hamada, and D. Nurseitov, “Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text,” Journal of Imaging 2020, Vol. 6, Page 141, vol. 6, no. 12, p. 141, Dec. 2020, doi: 10.3390/JIMAGING6120141.
[27] R. Ptucha, F. Petroski Such, S. Pillai, F. Brockler, V. Singh, and P. Hutkowski, “Intelligent character recognition using fully convolutional neural networks,” Pattern Recognit, vol. 88, pp. 604–613, Apr. 2019, doi: 10.1016/j.patcog.2018.12.017.
[28] P. Dreuw, P. Doetsch, C. Plahl, and H. Ney, “Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition,” Proceedings - International Conference on Image Processing, ICIP, pp. 3541–3544, 2011, doi: 10.1109/ICIP.2011.6116480.
[29] M. Kozielski, P. Doetsch, H. N.-2013 12th International, and undefined 2013, “Improvements in rwth’s system for off-line handwriting recognition,” ieeexplore.ieee.orgM Kozielski, P Doetsch, H Ney2013 12th International Conference on Document Analysis and, 2013•ieeexplore.ieee.org, pp. 935–939, 2013, doi: 10.1109/ICDAR.2013.190.
[30] A. Vinciarelli and J. Luettin, “A new normalization technique for cursive handwritten words,” Pattern Recognit Lett, vol. 22, no. 9, pp. 1043–1050, Jul. 2001, doi: 10.1016/S0167-8655(01)00042-3.
[31] M. Kozielski, P. Doetsch, and H. Ney, “Improvements in RWTH’s system for off-line handwriting recognition,” Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 935–939, 2013, doi: 10.1109/ICDAR.2013.190.
[32] S. Ahlawat, A. Choudhary, A. Nayyar, S. Singh, and B. Yoon, “Improved handwritten digit recognition using convolutional neural networks (Cnn),” Sensors (Switzerland), vol. 20, no. 12, pp. 1–18, Jun. 2020, doi: 10.3390/s20123344.
[33] M. B. Bora, D. Daimary, K. Amitab, and D. Kandar, “Handwritten Character Recognition from Images using CNN-ECOC,” Procedia Comput Sci, vol. 167, pp. 2403–2409, Jan. 2020, doi: 10.1016/J.PROCS.2020.03.293.
[34] A. Poznanski and L. Wolf, “CNN-N-Gram for Handwriting Word Recognition,” 2016.
[35] T. Bluche and R. Messina, “Gated Convolutional Recurrent Neural Networks for Multilingual Handwriting Recognition,” Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, pp. 646–651, Jul. 2017, doi: 10.1109/ICDAR.2017.111.
[36] J. Wang and X. Hu, “Gated Recurrent Convolution Neural Network for OCR,” Adv Neural Inf Process Syst, vol. 30, 2017.
[37] H. Nisa, “Off-line Handwritten Text Recognition with Cross-outs and Corrections Using Deep Learning,” 2023.
[38] O. Tüselmann and G. A. Fink, “Neural models for semantic analysis of handwritten document images,” International Journal on Document Analysis and Recognition, 2024, doi: 10.1007/s10032-024-00477-8.
[39] L. Kang, P. Riba, M. Rusiñol, A. Fornés, and M. Villegas, “Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition”.
[40] M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman, “Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition,” Jun. 2014, doi: 10.48550/arxiv.1406.2227.
[41] S. I. Google, “Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models,” Adv Neural Inf Process Syst, vol. 30, 2017.
[42] P. Dreuw, P. Doetsch, C. Plahl, and H. Ney, “Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition,” Proceedings - International Conference on Image Processing, ICIP, pp. 3541–3544, 2011, doi: 10.1109/ICIP.2011.6116480.
