A Restoration and Segmentation Unit for the Historic Persian Documents
This paper aims to provide a document restoration and segmentation algorithm for the Historic Middle Persian or Pahlavi manuscripts. The proposed algorithm uses the mathematical morphology and connected component concept to segment the line, word, and character overlapped in the Middle-age Persian d...
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| creator | Alirezaee, Shahpour Fard, Alireza Shayesteh Aghaeinia, Hassan Faez, Karim |
| description | This paper aims to provide a document restoration and segmentation algorithm for the Historic Middle Persian or Pahlavi manuscripts. The proposed algorithm uses the mathematical morphology and connected component concept to segment the line, word, and character overlapped in the Middle-age Persian documents in preparation for OCR application. To evaluate the performance of the restoration algorithm, 200 pages of the Pahlavi documents are used as experimental data in our test. Numerical results indicate that the proposed algorithm can remove the noise and destructive effects. The results also show 99.14% accuracy on the baseline detection, 97.35% accuracy on the text line extraction and removing other lines overlaps, and 99.5% accuracy for segmenting the extracted text lines to their components. |
| doi_str_mv | 10.1007/11558484_85 |
| format | Conference Proceeding |
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The proposed algorithm uses the mathematical morphology and connected component concept to segment the line, word, and character overlapped in the Middle-age Persian documents in preparation for OCR application. To evaluate the performance of the restoration algorithm, 200 pages of the Pahlavi documents are used as experimental data in our test. Numerical results indicate that the proposed algorithm can remove the noise and destructive effects. The results also show 99.14% accuracy on the baseline detection, 97.35% accuracy on the text line extraction and removing other lines overlaps, and 99.5% accuracy for segmenting the extracted text lines to their components.</description><edition>1ère éd</edition><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540290322</identifier><identifier>ISBN: 354029032X</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540320463</identifier><identifier>EISBN: 3540320466</identifier><identifier>DOI: 10.1007/11558484_85</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Electrical Engineer Department ; Exact sciences and technology ; Noise Removal ; Pattern recognition. Digital image processing. Computational geometry ; Renyi Entropy ; Speech and sound recognition and synthesis. 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The proposed algorithm uses the mathematical morphology and connected component concept to segment the line, word, and character overlapped in the Middle-age Persian documents in preparation for OCR application. To evaluate the performance of the restoration algorithm, 200 pages of the Pahlavi documents are used as experimental data in our test. Numerical results indicate that the proposed algorithm can remove the noise and destructive effects. The results also show 99.14% accuracy on the baseline detection, 97.35% accuracy on the text line extraction and removing other lines overlaps, and 99.5% accuracy for segmenting the extracted text lines to their components.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Electrical Engineer Department</subject><subject>Exact sciences and technology</subject><subject>Noise Removal</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Renyi Entropy</subject><subject>Speech and sound recognition and synthesis. Linguistics</subject><subject>Text Line</subject><subject>Word Segmentation</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540290322</isbn><isbn>354029032X</isbn><isbn>9783540320463</isbn><isbn>3540320466</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkE1LAzEQhuMXWGpP_oFcPHhYzeRjkxxLtVYoKGrPIdlN6mqbLcl68N-bUhHnMAPv-zDDvAhdArkBQuQtgBCKK26UOEITLRUTnDBKeM2O0QhqgIoxrk_-PKqLTU_RiDBCKy05O0eTnD9IKQZCghih-RS_-Dz0yQ5dH7GNLX71662Pw0FYxW7AoU94ePd40e3JrsHPPuXORnzXN197Nl-gs2A32U9-5xit5vdvs0W1fHp4nE2X1Y6CHipVOy6kK8cJJ7K1ilgna6dCK1rNIZDQEhCal6csdyy01FmqnKdcFwECG6Orw96dzY3dhGRj02WzS93Wpm8DkhW09DG6PnC5WHHtk3F9_5kNELPP0vzLkv0An_1foA</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Alirezaee, Shahpour</creator><creator>Fard, Alireza Shayesteh</creator><creator>Aghaeinia, Hassan</creator><creator>Faez, Karim</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>A Restoration and Segmentation Unit for the Historic Persian Documents</title><author>Alirezaee, Shahpour ; Fard, Alireza Shayesteh ; Aghaeinia, Hassan ; Faez, Karim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-86b457b3150407da80ab76b8fd5d941f0fd01594204a4b3fd2ba28be2494a41f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Electrical Engineer Department</topic><topic>Exact sciences and technology</topic><topic>Noise Removal</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Renyi Entropy</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Text Line</topic><topic>Word Segmentation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alirezaee, Shahpour</creatorcontrib><creatorcontrib>Fard, Alireza Shayesteh</creatorcontrib><creatorcontrib>Aghaeinia, Hassan</creatorcontrib><creatorcontrib>Faez, Karim</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alirezaee, Shahpour</au><au>Fard, Alireza Shayesteh</au><au>Aghaeinia, Hassan</au><au>Faez, Karim</au><au>Blanc-Talon, Jacques</au><au>Popescu, Dan</au><au>Philips, Wilfried</au><au>Scheunders, Paul</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Restoration and Segmentation Unit for the Historic Persian Documents</atitle><btitle>Advanced Concepts for Intelligent Vision Systems</btitle><date>2005</date><risdate>2005</risdate><spage>674</spage><epage>680</epage><pages>674-680</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540290322</isbn><isbn>354029032X</isbn><eisbn>9783540320463</eisbn><eisbn>3540320466</eisbn><abstract>This paper aims to provide a document restoration and segmentation algorithm for the Historic Middle Persian or Pahlavi manuscripts. The proposed algorithm uses the mathematical morphology and connected component concept to segment the line, word, and character overlapped in the Middle-age Persian documents in preparation for OCR application. To evaluate the performance of the restoration algorithm, 200 pages of the Pahlavi documents are used as experimental data in our test. Numerical results indicate that the proposed algorithm can remove the noise and destructive effects. The results also show 99.14% accuracy on the baseline detection, 97.35% accuracy on the text line extraction and removing other lines overlaps, and 99.5% accuracy for segmenting the extracted text lines to their components.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11558484_85</doi><tpages>7</tpages><edition>1ère éd</edition></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advanced Concepts for Intelligent Vision Systems, 2005, p.674-680 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17324917 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Electrical Engineer Department Exact sciences and technology Noise Removal Pattern recognition. Digital image processing. Computational geometry Renyi Entropy Speech and sound recognition and synthesis. Linguistics Text Line Word Segmentation |
| title | A Restoration and Segmentation Unit for the Historic Persian Documents |
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