Abstract
This paper covers part of a study which concerns the feasibility of real-time word spotting with partially recurrent neural networks (PRNN’s). PRNN’s have already proven appropriate for other examples of pure sequence recognition [1, 2]. However choices concerning architectural and learning aspects are still hard to make. One of the questions still to be answered, is how these aspects influence the term of memory of a PRNN. This paper tries to obtain some directives regarding architectures and learning algorithms.
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© 1995 Springer-Verlag London Limited
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v. Leeuwen, D., Wittenburg, P., Poel, M. (1995). Appropriate Context Association and Learning Parameters for Word Spotting with Partially Recurrent Neural Networks. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_48
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_48
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
Online ISBN: 978-1-4471-3087-1
eBook Packages: Springer Book Archive