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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Código do Detentoribi 8JMKD3MGPEW34M/46T9EHH
Identificador8JMKD3MGPEW34M/4388QM2
Repositóriosid.inpe.br/sibgrapi/2020/09.10.14.33
Última Atualização2020:10.01.19.49.53 (UTC) administrator
Repositório de Metadadossid.inpe.br/sibgrapi/2020/09.10.14.33.12
Última Atualização dos Metadados2022:06.14.00.00.00 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00016
Chave de CitaçãoSouzaNetoBezeToseLima:2020:DeLeSy
TítuloHTR-Flor: a deep learning system for offline handwritten text recognition
FormatoOn-line
Ano2020
Data de Acesso17 maio 2024
Número de Arquivos1
Tamanho957 KiB
2. Contextualização
Autor1 Souza Neto, Arthur Flor de
2 Bezerra, Byron Leite Dantas
3 Toselli, Alejandro Hector
4 Lima, Estanislau Baptista
Afiliação1 Universidade de Pernambuco
2 Universidade de Pernambuco
3 Universitat Politecnica de Valencia
4 Universidade de Pernambuco
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
Endereço de e-Mailbyron.leite@upe.br
Nome do EventoConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Localização do EventoPorto de Galinhas (virtual)
Data7-10 Nov. 2020
Editora (Publisher)IEEE Computer Society
Cidade da EditoraLos Alamitos
Título do LivroProceedings
Tipo TerciárioFull Paper
Histórico (UTC)2020-10-01 19:49:54 :: byron.leite@upe.br -> administrator :: 2020
2022-06-14 00:00:00 :: administrator -> byron.leite@upe.br :: 2020
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo de Versãofinaldraft
Palavras-ChaveHandwritten Text Recognition
Gated Convolutional Neural Networks
Gated CNN
Deep Neural Networks
ResumoIn recent years, Handwritten Text Recognition (HTR) has captured a lot of attention among the researchers of the computer vision community. Current state-of-the-art approaches for offline HTR are based on Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, deep models such as CRNNs, Recurrent Neural Networks (RNNs) are likely to suffer from vanishing/exploding gradient problems when processing long text images, which are commonly found in scanned documents. Besides, they usually have millions of parameters which require huge amount of data, and computational resource. Recently, a new class of neural network architecture, called Gated Convolutional Neural Networks (Gated-CNN), has demonstrated potentials to complement CRNN methods in modeling. Therefore, in this paper, we present a new architecture for HTR, based on Gated-CNN, with fewer parameters and fewer layers, which is able to outperform the current state-of-the-art architectures for HTR. The experiment validates that the proposed model has statistically significant recognition results, surpassing previous HTR systems by an average of 33% over five important handwritten benchmark datasets. Moreover, the proposed model is able to achieve satisfactory recognition rates even in case of few training data. Finally, its compact architecture requires less computational resources, which can be applied for real-world applications that have hardware limitations, such as robots and smartphones.
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Conteúdo da Pasta docacessar
Conteúdo da Pasta source
PID6607213.pdf 10/09/2020 11:33 956.6 KiB 
Conteúdo da Pasta agreement
agreement.html 10/09/2020 11:33 1.2 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGPEW34M/4388QM2
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGPEW34M/4388QM2
Idiomaen
Arquivo AlvoPID6607213.pdf
Grupo de Usuáriosbyron.leite@upe.br
Visibilidadeshown
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Repositório Espelhosid.inpe.br/banon/2001/03.30.15.38.24
Unidades Imediatamente Superiores8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Lista de Itens Citandosid.inpe.br/sibgrapi/2020/10.28.20.46 6
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
Acervo Hospedeirosid.inpe.br/banon/2001/03.30.15.38
6. Notas
Campos Vaziosarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Controle da descrição
e-Mail (login)byron.leite@upe.br
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