Publicacions CVC -- Edit Record

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	You must login to submit this form! Login Quick Search: Field: contains: ...
	Edit the following record:

Author	...				is Editor
Title	...			Type
Year	...	Publication	...	Abbreviated Journal	...
Volume	...	Issue	...	Pages	...
Keywords	...
Abstract	The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios.
Address	...
Corporate Author	...			Thesis
Publisher	...	Place of Publication	...	Editor	...
Language	...	Summary Language	...	Original Title	...
Series Editor	...	Series Title	...	Abbreviated Series Title	...
Series Volume	...	Series Issue	...	Edition	...
ISSN	...	ISBN	...	Medium	...
Area	...	Expedition	...	Conference	...
Notes	...			Approved	yes no
Location
Call Number	...			Serial
Marked	yes no	Copy		Selected	yes no
User Keys	...
User Notes	...			User File	...
User Groups	...			Cite Key	...
Related	...
File
URL	...			DOI	...
	Online publication. Cite with this text: ...

Location Field:	my name & email address

Home

SQL Search | Library Search | Show Record | Extract Citations

Help