CMATERdbl: a database of unconstrained handwritten Bangla and Bangia-English mixed script document image
Identifieur interne : 000086 ( PascalFrancis/Checkpoint ); précédent : 000085; suivant : 000087CMATERdbl: a database of unconstrained handwritten Bangla and Bangia-English mixed script document image
Auteurs : Ram Sarkar [Inde] ; Nibaran Das [Inde] ; Subhadip Basu [Inde] ; Mahantapas Kundu [Inde] ; Mita Nasipuri [Inde] ; Dipak Kumar Basu [Inde]Source :
- International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2012.
Descripteurs français
- Pascal (Inist)
- Base de données, Caractère manuscrit, En ligne, Reconnaissance optique caractère, Reconnaissance caractère, Texte, Logiciel libre, Traitement image, Banque image, Extraction information, Hors ligne, Document manuscrit, Multilinguisme, Communication écrite, Réalité terrain, Méthode indirecte, Annotation, Extraction forme, ., Traitement image document, Segmentation image.
- Wicri :
- topic : Base de données, Multilinguisme.
English descriptors
- KwdEn :
- Annotation, Character recognition, Database, Document image processing, Ground truth, Image databank, Image processing, Image segmentation, Indirect method, Information extraction, Manuscript character, Manuscript document, Multilingualism, Off line, On line, Open source software, Optical character recognition, Pattern extraction, Text, Written communication.
Abstract
In this paper, we have described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangla text mixed with English words. This is the first handwritten database in this area, as mentioned above, available as an open source document. As India is a multi-lingual country and has a colonial past, so multi-script document pages are very much common. The database contains 150 handwritten document pages, among which 100 pages are written purely in Bangla script and rests of the 50 pages are written in Bangla text mixed with English words. This database for off-line-handwritten scripts is collected from different data sources. After collecting the document pages, all the documents have been preprocessed and distributed into two groups, i.e., CMATERdb1.1.1, containing document pages written in Bangla script only, and CMATERdb1.2.1, containing document pages written in Bangla text mixed with English words. Finally, we have also provided the useful ground truth images for the line segmentation purpose. To generate the ground truth images, we have first labeled each line in a document page automatically by applying one of our previously developed line extraction techniques [Khandelwal et al., PReMI 2009, pp. 369-374] and then corrected any possible error by using our developed tool GT Gen 1.1. Line extraction accuracies of 90.6 and 92.38% are achieved on the two databases, respectively, using our algorithm. Both the databases along with the ground truth annotations and the ground truth generating tool are available freely at.
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
Pascal:12-0415342Le document en format XML
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Emeritus Fellow, Computer Science and Engineering Department, Jadavpur University</s1><s2>Kolkata 700032</s2><s3>IND</s3><sZ>6 aut.</sZ></inist:fA14><country>Inde</country><wicri:noRegion>Kolkata 700032</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">International journal on document analysis and recognition : (Print)</title><title level="j" type="abbreviated">Int. j. doc. anal. recognit. : (Print)</title><idno type="ISSN">1433-2833</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">International journal on document analysis and recognition : (Print)</title><title level="j" type="abbreviated">Int. j. doc. anal. recognit. : (Print)</title><idno type="ISSN">1433-2833</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Annotation</term><term>Character recognition</term><term>Database</term><term>Document image processing</term><term>Ground truth</term><term>Image databank</term><term>Image processing</term><term>Image segmentation</term><term>Indirect method</term><term>Information extraction</term><term>Manuscript character</term><term>Manuscript document</term><term>Multilingualism</term><term>Off line</term><term>On line</term><term>Open source software</term><term>Optical character recognition</term><term>Pattern extraction</term><term>Text</term><term>Written communication</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Base de données</term><term>Caractère manuscrit</term><term>En ligne</term><term>Reconnaissance optique caractère</term><term>Reconnaissance caractère</term><term>Texte</term><term>Logiciel libre</term><term>Traitement image</term><term>Banque image</term><term>Extraction information</term><term>Hors ligne</term><term>Document manuscrit</term><term>Multilinguisme</term><term>Communication écrite</term><term>Réalité terrain</term><term>Méthode indirecte</term><term>Annotation</term><term>Extraction forme</term><term>.</term><term>Traitement image document</term><term>Segmentation image</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Base de données</term><term>Multilinguisme</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we have described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangla text mixed with English words. This is the first handwritten database in this area, as mentioned above, available as an open source document. As India is a multi-lingual country and has a colonial past, so multi-script document pages are very much common. The database contains 150 handwritten document pages, among which 100 pages are written purely in Bangla script and rests of the 50 pages are written in Bangla text mixed with English words. This database for off-line-handwritten scripts is collected from different data sources. After collecting the document pages, all the documents have been preprocessed and distributed into two groups, i.e., CMATERdb1.1.1, containing document pages written in Bangla script only, and CMATERdb1.2.1, containing document pages written in Bangla text mixed with English words. Finally, we have also provided the useful ground truth images for the line segmentation purpose. To generate the ground truth images, we have first labeled each line in a document page automatically by applying one of our previously developed line extraction techniques [Khandelwal et al., PReMI 2009, pp. 369-374] and then corrected any possible error by using our developed tool GT Gen 1.1. Line extraction accuracies of 90.6 and 92.38% are achieved on the two databases, respectively, using our algorithm. Both the databases along with the ground truth annotations and the ground truth generating tool are available freely at.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>1433-2833</s0></fA01><fA03 i2="1"><s0>Int. j. doc. anal. recognit. : (Print)</s0></fA03><fA05><s2>15</s2></fA05><fA06><s2>1</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>CMATERdbl: a database of unconstrained handwritten Bangla and Bangia-English mixed script document image</s1></fA08><fA11 i1="01" i2="1"><s1>SARKAR (Ram)</s1></fA11><fA11 i1="02" i2="1"><s1>DAS (Nibaran)</s1></fA11><fA11 i1="03" i2="1"><s1>BASU (Subhadip)</s1></fA11><fA11 i1="04" i2="1"><s1>KUNDU (Mahantapas)</s1></fA11><fA11 i1="05" i2="1"><s1>NASIPURI (Mita)</s1></fA11><fA11 i1="06" i2="1"><s1>KUMAR BASU (Dipak)</s1></fA11><fA14 i1="01"><s1>Computer Science and Engineering Department, Jadavpur University</s1><s2>Kolkata 700032</s2><s3>IND</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ><sZ>5 aut.</sZ></fA14><fA14 i1="02"><s1>A.I.C.T.E. Emeritus Fellow, Computer Science and Engineering Department, Jadavpur University</s1><s2>Kolkata 700032</s2><s3>IND</s3><sZ>6 aut.</sZ></fA14><fA20><s1>71-83</s1></fA20><fA21><s1>2012</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA43 i1="01"><s1>INIST</s1><s2>26790</s2><s5>354000508427810050</s5></fA43><fA44><s0>0000</s0><s1>© 2012 INIST-CNRS. All rights reserved.</s1></fA44><fA45><s0>34 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>12-0415342</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>International journal on document analysis and recognition : (Print)</s0></fA64><fA66 i1="01"><s0>DEU</s0></fA66><fC01 i1="01" l="ENG"><s0>In this paper, we have described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangla text mixed with English words. This is the first handwritten database in this area, as mentioned above, available as an open source document. As India is a multi-lingual country and has a colonial past, so multi-script document pages are very much common. The database contains 150 handwritten document pages, among which 100 pages are written purely in Bangla script and rests of the 50 pages are written in Bangla text mixed with English words. This database for off-line-handwritten scripts is collected from different data sources. After collecting the document pages, all the documents have been preprocessed and distributed into two groups, i.e., CMATERdb1.1.1, containing document pages written in Bangla script only, and CMATERdb1.2.1, containing document pages written in Bangla text mixed with English words. Finally, we have also provided the useful ground truth images for the line segmentation purpose. To generate the ground truth images, we have first labeled each line in a document page automatically by applying one of our previously developed line extraction techniques [Khandelwal et al., PReMI 2009, pp. 369-374] and then corrected any possible error by using our developed tool GT Gen 1.1. Line extraction accuracies of 90.6 and 92.38% are achieved on the two databases, respectively, using our algorithm. Both the databases along with the ground truth annotations and the ground truth generating tool are available freely at.</s0></fC01><fC02 i1="01" i2="X"><s0>001D02C03</s0></fC02><fC02 i1="02" i2="X"><s0>001D02B07D</s0></fC02><fC03 i1="01" i2="X" l="FRE"><s0>Base de données</s0><s5>06</s5></fC03><fC03 i1="01" i2="X" l="ENG"><s0>Database</s0><s5>06</s5></fC03><fC03 i1="01" i2="X" l="SPA"><s0>Base dato</s0><s5>06</s5></fC03><fC03 i1="02" i2="X" l="FRE"><s0>Caractère manuscrit</s0><s5>07</s5></fC03><fC03 i1="02" i2="X" l="ENG"><s0>Manuscript character</s0><s5>07</s5></fC03><fC03 i1="02" i2="X" l="SPA"><s0>Carácter manuscrito</s0><s5>07</s5></fC03><fC03 i1="03" i2="X" l="FRE"><s0>En ligne</s0><s5>08</s5></fC03><fC03 i1="03" i2="X" l="ENG"><s0>On line</s0><s5>08</s5></fC03><fC03 i1="03" i2="X" l="SPA"><s0>En línea</s0><s5>08</s5></fC03><fC03 i1="04" i2="X" l="FRE"><s0>Reconnaissance optique caractère</s0><s5>09</s5></fC03><fC03 i1="04" i2="X" l="ENG"><s0>Optical character 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uniqKey="Sarkar R" first="Ram" last="Sarkar">Ram Sarkar</name></noRegion><name sortKey="Basu, Subhadip" sort="Basu, Subhadip" uniqKey="Basu S" first="Subhadip" last="Basu">Subhadip Basu</name><name sortKey="Das, Nibaran" sort="Das, Nibaran" uniqKey="Das N" first="Nibaran" last="Das">Nibaran Das</name><name sortKey="Kumar Basu, Dipak" sort="Kumar Basu, Dipak" uniqKey="Kumar Basu D" first="Dipak" last="Kumar Basu">Dipak Kumar Basu</name><name sortKey="Kundu, Mahantapas" sort="Kundu, Mahantapas" uniqKey="Kundu M" first="Mahantapas" last="Kundu">Mahantapas Kundu</name><name sortKey="Nasipuri, Mita" sort="Nasipuri, Mita" uniqKey="Nasipuri M" first="Mita" last="Nasipuri">Mita Nasipuri</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|wiki= Ticri/CIDE
|area= OcrV1
|flux= PascalFrancis
|étape= Checkpoint
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|texte= CMATERdbl: a database of unconstrained handwritten Bangla and Bangia-English mixed script document image
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