Exploring recursive neural networks for compact handwritten text recognition models

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Exploring recursive neural networks for compact handwritten text recognition models ; volume:27 ; number:3 ; day:27 ; month:6 ; year:2024 ; pages:213-223 ; date:9.2024
International journal on document analysis and recognition ; 27, Heft 3 (27.6.2024), 213-223, 9.2024

Creator: Mas-Candela, Enrique
Calvo-Zaragoza, Jorge

Contributor: SpringerLink (Online service)

DOI: 10.1007/s10032-024-00481-y

URN: urn:nbn:de:101:1-2411251013066.831675358975

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:37 AM CEST

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Mas-Candela, Enrique
Calvo-Zaragoza, Jorge
SpringerLink (Online service)

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Lampung handwritten character recognition

Introduction in handwritten text recognition

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Mechanical Metamaterials for Handwritten Digits Recognition

Handwritten stenography recognition and the LION dataset

Discrete representation learning for handwritten text recognition

Arrow R-CNN for handwritten diagram recognition

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Lampung handwritten character recognition

Introduction in handwritten text recognition

Online recognition of handwritten music symbols

Mechanical Metamaterials for Handwritten Digits Recognition

Handwritten stenography recognition and the LION dataset

Discrete representation learning for handwritten text recognition

Arrow R-CNN for handwritten diagram recognition

Handwritten Chinese character recognition using fuzzy image alignment

Self-training for handwritten word recognition and retrieval

Enhancing handwritten text recognition accuracy with gated mechanisms

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Exploring recursive neural networks for compact handwritten text recognition models

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Other Objects (12)

Lampung handwritten character recognition

Introduction in handwritten text recognition

Online recognition of handwritten music symbols

Mechanical Metamaterials for Handwritten Digits Recognition

Handwritten stenography recognition and the LION dataset

Discrete representation learning for handwritten text recognition

Arrow R-CNN for handwritten diagram recognition

Arrow R-CNN for handwritten diagram recognition

Handwritten Chinese character recognition using fuzzy image alignment

Self-training for handwritten word recognition and retrieval

Self-training for handwritten word recognition and retrieval

Enhancing handwritten text recognition accuracy with gated mechanisms

Lampung handwritten character recognition

Introduction in handwritten text recognition

Online recognition of handwritten music symbols

Mechanical Metamaterials for Handwritten Digits Recognition

Handwritten stenography recognition and the LION dataset

Discrete representation learning for handwritten text recognition

Arrow R-CNN for handwritten diagram recognition

Arrow R-CNN for handwritten diagram recognition

Handwritten Chinese character recognition using fuzzy image alignment

Self-training for handwritten word recognition and retrieval

Self-training for handwritten word recognition and retrieval

Enhancing handwritten text recognition accuracy with gated mechanisms

Lampung handwritten character recognition

Introduction in handwritten text recognition

Online recognition of handwritten music symbols

Mechanical Metamaterials for Handwritten Digits Recognition

Handwritten stenography recognition and the LION dataset

Discrete representation learning for handwritten text recognition

Arrow R-CNN for handwritten diagram recognition

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Handwritten Chinese character recognition using fuzzy image alignment

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Self-training for handwritten word recognition and retrieval

Enhancing handwritten text recognition accuracy with gated mechanisms

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