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All issues Volume 392 (2024) MATEC Web Conf., 392 (2024) 01126 References
Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01126
Number of page(s) 8
DOI https://doi.org/10.1051/matecconf/202439201126
Published online 18 March 2024
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  7. ”Pattern Recognition and Machine Learning” by Christopher M. Bishop. Provides a foundational understanding of pattern recognition techniques used in HTR. [Google Scholar]
  8. ”Handbook of Document Image Processing and Recognition” by David Doermann and Karl Tombre. Covers a wide range of topics related to document image processing and HTR. [Google Scholar]
  9. ”Optical Character Recognition: An Illustrated Guide to the Frontier” by Stephen V. Rice. Offers insights into OCR and HTR technologies with a focus on illustrations and examples. [Google Scholar]
  10. ”A Survey of Deep Learning in Optical Character Recognition” by Shreya Khare and Mayank Sharma. An extensive survey of deep learning techniques in OCR and HTR. [Google Scholar]
  11. ”Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks” by Alex Graves, Santiago Fernández, and Jürgen Schmidhuber. Introduces the CTC algorithm, crucial for sequence labeling in HTR. [Google Scholar]
  12. ”Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks” by Alex Graves et al. Discusses the use of multidimensional LSTM networks for offline HTR. [Google Scholar]
  13. ”Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks” by Jan Koutník et al. Explores the application of large LSTM networks in HTR. [Google Scholar]
  14. ”A Novel Approach to On-Line Handwriting Recognition Based on Bidirectional Long Short-Term Memory Networks” by A. Graves et al. Introduces bidirectional LSTM networks for online HTR. [Google Scholar]
  15. ”Robust Reading with Synthetic Data Engineered for Scene Text” by Max Jaderberg et al. Discusses the use of synthetic data for training HTR models. [Google Scholar]
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  19. ”Data augmentation for recognition of handwritten words and lines using a CNN-LSTM network,” by C. Wigington, S. Stewart, B. L. Davis, B. Barrett, B. L. Price, and S. Cohen, was published in Proc. Int. Conf. Document Anal. Recognit., 2017, pp. 639–645. [Google Scholar]
  20. B. Stuner, C. Chatelain, and T. Paquet, “Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon,” Multimedia Tools Appl., vol. 79, pp. 34407–34427, 2020. [CrossRef] [Google Scholar]
  21. L. von Rueden et al., “Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems,” in IEEE Transactions on Knowledge andData Engineering, doi: 10.1109/TKDE.2021.3079836. [Google Scholar]
  22. K. S. Bohnsack, M. Kaden, J. Abel and T. Villmann, “Alignment-free sequence comparison: A systematic survey from a machine learning perspective,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2022.3140873. [Google Scholar]
  23. ”Generative Adversarial Networks for Text Generation” by Samuel R. Bowman et al. Introduces the concept of using GANs for text generation, which can be applied to HTR. [Google Scholar]
  24. ”Word Beam Search: A Connectionist Temporal Classification Decoding Algorithm” by Philip R. Bontrager et al. Presents the Word Beam Search algorithm for improving HTR results. [Google Scholar]
  25. ”End-to-End Text Recognition with Convolutional Neural Networks” by M. Jaderberg et al. Discusses using CNNs for end-to-end text recognition, which is applicable to HTR. [Google Scholar]
  26. ”Efficient and Accurate Scene Text Detection with Extremely Deep Networks” by X. Zhou et al. [Google Scholar]
  27. ”Cascaded Segmentation-Recognition Models for Scene Text Recognition” by Xuejian Rong et al. [Google Scholar]
  28. T. Bluche, “Joint line segmentation and transcription for end-to- end handwritten paragraph recognition,” in Proc. Int. Conf. Neural Inf. Process. Syst., 2016, pp. 838–846. [Google Scholar]
  29. C. Wigington, C. Tensmeyer, B. L. Davis, W. A. Barrett, B. L. Price, and S. Cohen, “Start, follow, read: End-to- end full-page handwriting recognition,” in Proc. 15th Eur. Conf. Comput. Vis., 2018, pp. 372–388. [Google Scholar]
  30. In Proc. Int. Conf. Document Anal. Recognit., 2019, pp. 1–8, C. Tensmeyer and C. Wigington, “Training full-page handwritten text recognition models without annotated line breaks." [Google Scholar]

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