Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes
The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten doc...
Gespeichert in:
Veröffentlicht in: | Multimedia tools and applications 2021-04, Vol.80 (9), p.14075-14100 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 14100 |
---|---|
container_issue | 9 |
container_start_page | 14075 |
container_title | Multimedia tools and applications |
container_volume | 80 |
creator | Dhieb, Thameur Boubaker, Houcine Ouarda, Wael Njah, Sourour Ben Ayed, Mounir Alimi, Adel M. |
description | The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten documents are not generally stable and show a wide variability from the same person over time, or from different writers. The capacity to identify the documents’ writers provides further chances of using these handwritten documents for several applications like forensic science, control access, digital rights management and financial transactions. In this paper, we propose a novel system to text-independent online multilingual writer identification. Our system is based on new model that we named the Extended Beta-Elliptic Model. Moreover, we are interested in using the Fuzzy Elementary Perceptual Codes to characterize the handwriting of writers well. In addition, we adopted the use of Recurrent Neural Network with Deep Bidirectional Long Short-Term Memory in the training and identification phases. Experiments are conducted on IBM_UB_1 and ADAB datasets with 98.44% and 100% writer identification rates respectively. The proposed system using the combination of the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes in features extraction and the Deep Bidirectional Long Short-Term Memory in classification outperforms the existing online writer identification systems on both Latin and Arabic scripts. |
doi_str_mv | 10.1007/s11042-020-10412-8 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2517675694</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2517675694</sourcerecordid><originalsourceid>FETCH-LOGICAL-c368t-3f40fb823c5fa7a03a7ed9cb1426b639682cf6463c6a8e84c43c2b5e148a9b3a3</originalsourceid><addsrcrecordid>eNp9kc2OFSEQhTtGE8fRF3BF4hrlr4G71PE3mWQ2uiY0XVyZ0E0LdHTmgXxO63pN3M2KQ-o7B1JnGF5y9pozZt40zpkSlAlGUXBB7aPhgo9GUmMEf4xaWkbNyPjT4Vlrt4xxPQp1Mfx-D7CRKc2pQuiprD6TXNYjad9L7bRDXcgCS6l3JJZKyprTCmTZc0-ojjviP2tCjKQZ1p5iCv4UQybfYEae-JXArw7rjNd30D2FnNPWUyBLmSHjfCZxv7-_I5BhwQyPb21QA2z9FB-Qas-HJ9HnBi_-nZfDt48fvl59ptc3n75cvb2mQWrbqYyKxckKGcbojWfSG5gPYeJK6EnLg7YiRK20DNpbsCooGcQ0AlfWHybp5eXw6py71fJjh9bdbdkrLqU5MXKjzagPCilxpkItrVWIbqtpwX87ztypD3fuw2Ef7m8fzqJJnk0N4fUI9X_0A64_lTiSQQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2517675694</pqid></control><display><type>article</type><title>Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes</title><source>SpringerNature Journals</source><creator>Dhieb, Thameur ; Boubaker, Houcine ; Ouarda, Wael ; Njah, Sourour ; Ben Ayed, Mounir ; Alimi, Adel M.</creator><creatorcontrib>Dhieb, Thameur ; Boubaker, Houcine ; Ouarda, Wael ; Njah, Sourour ; Ben Ayed, Mounir ; Alimi, Adel M.</creatorcontrib><description>The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten documents are not generally stable and show a wide variability from the same person over time, or from different writers. The capacity to identify the documents’ writers provides further chances of using these handwritten documents for several applications like forensic science, control access, digital rights management and financial transactions. In this paper, we propose a novel system to text-independent online multilingual writer identification. Our system is based on new model that we named the Extended Beta-Elliptic Model. Moreover, we are interested in using the Fuzzy Elementary Perceptual Codes to characterize the handwriting of writers well. In addition, we adopted the use of Recurrent Neural Network with Deep Bidirectional Long Short-Term Memory in the training and identification phases. Experiments are conducted on IBM_UB_1 and ADAB datasets with 98.44% and 100% writer identification rates respectively. The proposed system using the combination of the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes in features extraction and the Deep Bidirectional Long Short-Term Memory in classification outperforms the existing online writer identification systems on both Latin and Arabic scripts.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-020-10412-8</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Access control ; Artificial intelligence ; Computer Communication Networks ; Computer Science ; Copy protection ; Data Structures and Information Theory ; Feature extraction ; Financial management ; Forensic science ; Handwriting ; Identification ; Multilingualism ; Multimedia Information Systems ; Pattern recognition ; Recurrent neural networks ; Short term ; Special Purpose and Application-Based Systems ; Writers</subject><ispartof>Multimedia tools and applications, 2021-04, Vol.80 (9), p.14075-14100</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-3f40fb823c5fa7a03a7ed9cb1426b639682cf6463c6a8e84c43c2b5e148a9b3a3</citedby><cites>FETCH-LOGICAL-c368t-3f40fb823c5fa7a03a7ed9cb1426b639682cf6463c6a8e84c43c2b5e148a9b3a3</cites><orcidid>0000-0001-9173-2204</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-020-10412-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-020-10412-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Dhieb, Thameur</creatorcontrib><creatorcontrib>Boubaker, Houcine</creatorcontrib><creatorcontrib>Ouarda, Wael</creatorcontrib><creatorcontrib>Njah, Sourour</creatorcontrib><creatorcontrib>Ben Ayed, Mounir</creatorcontrib><creatorcontrib>Alimi, Adel M.</creatorcontrib><title>Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten documents are not generally stable and show a wide variability from the same person over time, or from different writers. The capacity to identify the documents’ writers provides further chances of using these handwritten documents for several applications like forensic science, control access, digital rights management and financial transactions. In this paper, we propose a novel system to text-independent online multilingual writer identification. Our system is based on new model that we named the Extended Beta-Elliptic Model. Moreover, we are interested in using the Fuzzy Elementary Perceptual Codes to characterize the handwriting of writers well. In addition, we adopted the use of Recurrent Neural Network with Deep Bidirectional Long Short-Term Memory in the training and identification phases. Experiments are conducted on IBM_UB_1 and ADAB datasets with 98.44% and 100% writer identification rates respectively. The proposed system using the combination of the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes in features extraction and the Deep Bidirectional Long Short-Term Memory in classification outperforms the existing online writer identification systems on both Latin and Arabic scripts.</description><subject>Access control</subject><subject>Artificial intelligence</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Copy protection</subject><subject>Data Structures and Information Theory</subject><subject>Feature extraction</subject><subject>Financial management</subject><subject>Forensic science</subject><subject>Handwriting</subject><subject>Identification</subject><subject>Multilingualism</subject><subject>Multimedia Information Systems</subject><subject>Pattern recognition</subject><subject>Recurrent neural networks</subject><subject>Short term</subject><subject>Special Purpose and Application-Based Systems</subject><subject>Writers</subject><issn>1380-7501</issn><issn>1573-7721</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kc2OFSEQhTtGE8fRF3BF4hrlr4G71PE3mWQ2uiY0XVyZ0E0LdHTmgXxO63pN3M2KQ-o7B1JnGF5y9pozZt40zpkSlAlGUXBB7aPhgo9GUmMEf4xaWkbNyPjT4Vlrt4xxPQp1Mfx-D7CRKc2pQuiprD6TXNYjad9L7bRDXcgCS6l3JJZKyprTCmTZc0-ojjviP2tCjKQZ1p5iCv4UQybfYEae-JXArw7rjNd30D2FnNPWUyBLmSHjfCZxv7-_I5BhwQyPb21QA2z9FB-Qas-HJ9HnBi_-nZfDt48fvl59ptc3n75cvb2mQWrbqYyKxckKGcbojWfSG5gPYeJK6EnLg7YiRK20DNpbsCooGcQ0AlfWHybp5eXw6py71fJjh9bdbdkrLqU5MXKjzagPCilxpkItrVWIbqtpwX87ztypD3fuw2Ef7m8fzqJJnk0N4fUI9X_0A64_lTiSQQ</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Dhieb, Thameur</creator><creator>Boubaker, Houcine</creator><creator>Ouarda, Wael</creator><creator>Njah, Sourour</creator><creator>Ben Ayed, Mounir</creator><creator>Alimi, Adel M.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-9173-2204</orcidid></search><sort><creationdate>20210401</creationdate><title>Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes</title><author>Dhieb, Thameur ; Boubaker, Houcine ; Ouarda, Wael ; Njah, Sourour ; Ben Ayed, Mounir ; Alimi, Adel M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-3f40fb823c5fa7a03a7ed9cb1426b639682cf6463c6a8e84c43c2b5e148a9b3a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Access control</topic><topic>Artificial intelligence</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Copy protection</topic><topic>Data Structures and Information Theory</topic><topic>Feature extraction</topic><topic>Financial management</topic><topic>Forensic science</topic><topic>Handwriting</topic><topic>Identification</topic><topic>Multilingualism</topic><topic>Multimedia Information Systems</topic><topic>Pattern recognition</topic><topic>Recurrent neural networks</topic><topic>Short term</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Writers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dhieb, Thameur</creatorcontrib><creatorcontrib>Boubaker, Houcine</creatorcontrib><creatorcontrib>Ouarda, Wael</creatorcontrib><creatorcontrib>Njah, Sourour</creatorcontrib><creatorcontrib>Ben Ayed, Mounir</creatorcontrib><creatorcontrib>Alimi, Adel M.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dhieb, Thameur</au><au>Boubaker, Houcine</au><au>Ouarda, Wael</au><au>Njah, Sourour</au><au>Ben Ayed, Mounir</au><au>Alimi, Adel M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>80</volume><issue>9</issue><spage>14075</spage><epage>14100</epage><pages>14075-14100</pages><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>The development of pattern recognition and artificial intelligence domains owes the writer identification challenge greatly. In fact, writer identification is still a challenging task in the definition of a set of features able to characterize the various handwritten documents. These handwritten documents are not generally stable and show a wide variability from the same person over time, or from different writers. The capacity to identify the documents’ writers provides further chances of using these handwritten documents for several applications like forensic science, control access, digital rights management and financial transactions. In this paper, we propose a novel system to text-independent online multilingual writer identification. Our system is based on new model that we named the Extended Beta-Elliptic Model. Moreover, we are interested in using the Fuzzy Elementary Perceptual Codes to characterize the handwriting of writers well. In addition, we adopted the use of Recurrent Neural Network with Deep Bidirectional Long Short-Term Memory in the training and identification phases. Experiments are conducted on IBM_UB_1 and ADAB datasets with 98.44% and 100% writer identification rates respectively. The proposed system using the combination of the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes in features extraction and the Deep Bidirectional Long Short-Term Memory in classification outperforms the existing online writer identification systems on both Latin and Arabic scripts.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-020-10412-8</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0001-9173-2204</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1380-7501 |
ispartof | Multimedia tools and applications, 2021-04, Vol.80 (9), p.14075-14100 |
issn | 1380-7501 1573-7721 |
language | eng |
recordid | cdi_proquest_journals_2517675694 |
source | SpringerNature Journals |
subjects | Access control Artificial intelligence Computer Communication Networks Computer Science Copy protection Data Structures and Information Theory Feature extraction Financial management Forensic science Handwriting Identification Multilingualism Multimedia Information Systems Pattern recognition Recurrent neural networks Short term Special Purpose and Application-Based Systems Writers |
title | Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T12%3A23%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20bidirectional%20long%20short-term%20memory%20for%20online%20multilingual%20writer%20identification%20based%20on%20an%20extended%20Beta-elliptic%20model%20and%20fuzzy%20elementary%20perceptual%20codes&rft.jtitle=Multimedia%20tools%20and%20applications&rft.au=Dhieb,%20Thameur&rft.date=2021-04-01&rft.volume=80&rft.issue=9&rft.spage=14075&rft.epage=14100&rft.pages=14075-14100&rft.issn=1380-7501&rft.eissn=1573-7721&rft_id=info:doi/10.1007/s11042-020-10412-8&rft_dat=%3Cproquest_cross%3E2517675694%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2517675694&rft_id=info:pmid/&rfr_iscdi=true |