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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/4388QM2
Repositorysid.inpe.br/sibgrapi/2020/09.10.14.33
Last Update2020:10.01.19.49.53 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.10.14.33.12
Metadata Last Update2022:06.14.00.00.00 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00016
Citation KeySouzaNetoBezeToseLima:2020:DeLeSy
TitleHTR-Flor: a deep learning system for offline handwritten text recognition
FormatOn-line
Year2020
Access Date2024, Sep. 19
Number of Files1
Size957 KiB
2. Context
Author1 Souza Neto, Arthur Flor de
2 Bezerra, Byron Leite Dantas
3 Toselli, Alejandro Hector
4 Lima, Estanislau Baptista
Affiliation1 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)
e-Mail Addressbyron.leite@upe.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (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. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsHandwritten Text Recognition
Gated Convolutional Neural Networks
Gated CNN
Deep Neural Networks
AbstractIn 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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > HTR-Flor: a deep...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/4388QM2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4388QM2
Languageen
Target FilePID6607213.pdf
User Groupbyron.leite@upe.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 29
sid.inpe.br/sibgrapi/2022/06.10.21.49 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy 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. Description control
e-Mail (login)byron.leite@upe.br
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