PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification
Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this pape...
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| Veröffentlicht in: | Multimedia tools and applications 2018, Vol.77 (2), p.1643-1678 |
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| creator | Obaidullah, Sk Md Halder, Chayan Santosh, K. C. Das, Nibaran Roy, Kaushik |
| description | Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (
PHDIndic_11
), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.
PHDIndic_11
is composed of 1458 document text-pages written by 463 individuals from various parts of India. Further, we report the benchmark results for handwritten script identification (HSI). Beside script identification, the dataset can be effectively used in many other applications of document image analysis such as script sentence recognition/understanding, text-line segmentation, word segmentation/recognition, word spotting, handwritten and machine printed texts separation and writer identification. |
| doi_str_mv | 10.1007/s11042-017-4373-y |
| format | Article |
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PHDIndic_11
), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.
PHDIndic_11
is composed of 1458 document text-pages written by 463 individuals from various parts of India. Further, we report the benchmark results for handwritten script identification (HSI). Beside script identification, the dataset can be effectively used in many other applications of document image analysis such as script sentence recognition/understanding, text-line segmentation, word segmentation/recognition, word spotting, handwritten and machine printed texts separation and writer identification.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-017-4373-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Datasets ; Documents ; Handwriting ; Handwriting recognition ; Identification ; Image analysis ; Image segmentation ; Multimedia Information Systems ; Names ; Object recognition ; Postal codes ; Printed text ; Scripts ; Special Purpose and Application-Based Systems ; Writers</subject><ispartof>Multimedia tools and applications, 2018, Vol.77 (2), p.1643-1678</ispartof><rights>Springer Science+Business Media New York 2017</rights><rights>Springer Science+Business Media New York 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-6a16a3360f9deeeacbd40204d18b55b8b6a5620e1addc94f076ebafbec4c9f3e3</citedby><cites>FETCH-LOGICAL-c316t-6a16a3360f9deeeacbd40204d18b55b8b6a5620e1addc94f076ebafbec4c9f3e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-017-4373-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-017-4373-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Obaidullah, Sk Md</creatorcontrib><creatorcontrib>Halder, Chayan</creatorcontrib><creatorcontrib>Santosh, K. C.</creatorcontrib><creatorcontrib>Das, Nibaran</creatorcontrib><creatorcontrib>Roy, Kaushik</creatorcontrib><title>PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (
PHDIndic_11
), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.
PHDIndic_11
is composed of 1458 document text-pages written by 463 individuals from various parts of India. Further, we report the benchmark results for handwritten script identification (HSI). Beside script identification, the dataset can be effectively used in many other applications of document image analysis such as script sentence recognition/understanding, text-line segmentation, word segmentation/recognition, word spotting, handwritten and machine printed texts separation and writer identification.</description><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Data Structures and Information Theory</subject><subject>Datasets</subject><subject>Documents</subject><subject>Handwriting</subject><subject>Handwriting recognition</subject><subject>Identification</subject><subject>Image analysis</subject><subject>Image segmentation</subject><subject>Multimedia Information Systems</subject><subject>Names</subject><subject>Object recognition</subject><subject>Postal codes</subject><subject>Printed text</subject><subject>Scripts</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>2018</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>eNp1kEtPwzAQhCMEEqXwA7hZ4hzw5mEn3FB5tFIlOMDZcux1cZUmqe2C-u9xSSVOXHbn8M2sdpLkGugtUMrvPAAtspQCT4uc5-n-JJlAGQXnGZxGnVc05SWF8-TC-zWlwMqsmCTbt_njotNWCYB7MsgVpi1-YUs-Zae_nQ0BO6J7tdtgF4jdRIBoGaTHQHpDAOI0VlnZkt8Y4pWzQ_DE9O6oidXRayMlg-27y-TMyNbj1XFPk4_np_fZPF2-vixmD8tU5cBCyiQwmeeMmlojolSNLmhGCw1VU5ZN1TBZsowiSK1VXRjKGTbSNKgKVZsc82lyM-YOrt_u0Aex7neuiydFxgvGOavqKlIwUsr13js0YnDxS7cXQMWhWTE2K2Kz4tCs2EdPNnp8ZLsVur_k_00_CHV95w</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Obaidullah, Sk Md</creator><creator>Halder, Chayan</creator><creator>Santosh, K. 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C. ; Das, Nibaran ; Roy, Kaushik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-6a16a3360f9deeeacbd40204d18b55b8b6a5620e1addc94f076ebafbec4c9f3e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Data Structures and Information Theory</topic><topic>Datasets</topic><topic>Documents</topic><topic>Handwriting</topic><topic>Handwriting recognition</topic><topic>Identification</topic><topic>Image analysis</topic><topic>Image segmentation</topic><topic>Multimedia Information Systems</topic><topic>Names</topic><topic>Object recognition</topic><topic>Postal codes</topic><topic>Printed text</topic><topic>Scripts</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Writers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Obaidullah, Sk Md</creatorcontrib><creatorcontrib>Halder, Chayan</creatorcontrib><creatorcontrib>Santosh, K. C.</creatorcontrib><creatorcontrib>Das, Nibaran</creatorcontrib><creatorcontrib>Roy, Kaushik</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 Basic</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Obaidullah, Sk Md</au><au>Halder, Chayan</au><au>Santosh, K. C.</au><au>Das, Nibaran</au><au>Roy, Kaushik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2018</date><risdate>2018</risdate><volume>77</volume><issue>2</issue><spage>1643</spage><epage>1678</epage><pages>1643-1678</pages><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (
PHDIndic_11
), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.
PHDIndic_11
is composed of 1458 document text-pages written by 463 individuals from various parts of India. Further, we report the benchmark results for handwritten script identification (HSI). Beside script identification, the dataset can be effectively used in many other applications of document image analysis such as script sentence recognition/understanding, text-line segmentation, word segmentation/recognition, word spotting, handwritten and machine printed texts separation and writer identification.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-017-4373-y</doi><tpages>36</tpages></addata></record> |
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| subjects | Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Documents Handwriting Handwriting recognition Identification Image analysis Image segmentation Multimedia Information Systems Names Object recognition Postal codes Printed text Scripts Special Purpose and Application-Based Systems Writers |
| title | PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification |
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