Categorizing ancient documents
IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 2, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784 www.IJCSI.org The analysis of historical documents is still a topical issue given the importance of information that can be extracted and also the importance...
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| creator | Zaghden, Nizar Mullot, Remy Alimi, Mohamed Adel |
| description | IJCSI International Journal of Computer Science Issues, Vol. 10,
Issue 2, No 2, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784
www.IJCSI.org The analysis of historical documents is still a topical issue given the
importance of information that can be extracted and also the importance given
by the institutions to preserve their heritage. The main idea in order to
characterize the content of the images of ancient documents after attempting to
clean the image is segmented blocks texts from the same image and tries to find
similar blocks in either the same image or the entire image database. Most
approaches of offline handwriting recognition proceed by segmenting words into
smaller pieces (usually characters) which are recognized separately.
Recognition of a word then requires the recognition of all characters (OCR)
that compose it. Our work focuses mainly on the characterization of classes in
images of old documents. We use Som toolbox for finding classes in documents.
We applied also fractal dimensions and points of interest to categorize and
match ancient documents. |
| doi_str_mv | 10.48550/arxiv.1308.6311 |
| format | Article |
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Issue 2, No 2, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784
www.IJCSI.org The analysis of historical documents is still a topical issue given the
importance of information that can be extracted and also the importance given
by the institutions to preserve their heritage. The main idea in order to
characterize the content of the images of ancient documents after attempting to
clean the image is segmented blocks texts from the same image and tries to find
similar blocks in either the same image or the entire image database. Most
approaches of offline handwriting recognition proceed by segmenting words into
smaller pieces (usually characters) which are recognized separately.
Recognition of a word then requires the recognition of all characters (OCR)
that compose it. Our work focuses mainly on the characterization of classes in
images of old documents. We use Som toolbox for finding classes in documents.
We applied also fractal dimensions and points of interest to categorize and
match ancient documents.</description><identifier>DOI: 10.48550/arxiv.1308.6311</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2013-08</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1308.6311$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1308.6311$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Zaghden, Nizar</creatorcontrib><creatorcontrib>Mullot, Remy</creatorcontrib><creatorcontrib>Alimi, Mohamed Adel</creatorcontrib><title>Categorizing ancient documents</title><description>IJCSI International Journal of Computer Science Issues, Vol. 10,
Issue 2, No 2, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784
www.IJCSI.org The analysis of historical documents is still a topical issue given the
importance of information that can be extracted and also the importance given
by the institutions to preserve their heritage. The main idea in order to
characterize the content of the images of ancient documents after attempting to
clean the image is segmented blocks texts from the same image and tries to find
similar blocks in either the same image or the entire image database. Most
approaches of offline handwriting recognition proceed by segmenting words into
smaller pieces (usually characters) which are recognized separately.
Recognition of a word then requires the recognition of all characters (OCR)
that compose it. Our work focuses mainly on the characterization of classes in
images of old documents. We use Som toolbox for finding classes in documents.
We applied also fractal dimensions and points of interest to categorize and
match ancient documents.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotjrsOgkAURLexMGhvRfgBcO8u-6A0xFdCYkNPLrCQTQQMoFG_XlCrmSnO5BCyARqEWgi6xf5pHwFwqgPJAZbEjXE0ddfbt21rD9vCmnb0yq64N1MZVmRR4XUw6386JD3s0_jkJ5fjOd4lPkoBvhBhgUZyZpjiuZqmQGUUj5SglKNkWpc6knmkjGC0BBpqyBljAFBNVMEd4v5uv37ZrbcN9q9s9sxmT_4Bgv81VQ</recordid><startdate>20130828</startdate><enddate>20130828</enddate><creator>Zaghden, Nizar</creator><creator>Mullot, Remy</creator><creator>Alimi, Mohamed Adel</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20130828</creationdate><title>Categorizing ancient documents</title><author>Zaghden, Nizar ; Mullot, Remy ; Alimi, Mohamed Adel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a651-554cae632e273b75545a7e73975003a6288d896b97e520d10481b222111f632c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Zaghden, Nizar</creatorcontrib><creatorcontrib>Mullot, Remy</creatorcontrib><creatorcontrib>Alimi, Mohamed Adel</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zaghden, Nizar</au><au>Mullot, Remy</au><au>Alimi, Mohamed Adel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Categorizing ancient documents</atitle><date>2013-08-28</date><risdate>2013</risdate><abstract>IJCSI International Journal of Computer Science Issues, Vol. 10,
Issue 2, No 2, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784
www.IJCSI.org The analysis of historical documents is still a topical issue given the
importance of information that can be extracted and also the importance given
by the institutions to preserve their heritage. The main idea in order to
characterize the content of the images of ancient documents after attempting to
clean the image is segmented blocks texts from the same image and tries to find
similar blocks in either the same image or the entire image database. Most
approaches of offline handwriting recognition proceed by segmenting words into
smaller pieces (usually characters) which are recognized separately.
Recognition of a word then requires the recognition of all characters (OCR)
that compose it. Our work focuses mainly on the characterization of classes in
images of old documents. We use Som toolbox for finding classes in documents.
We applied also fractal dimensions and points of interest to categorize and
match ancient documents.</abstract><doi>10.48550/arxiv.1308.6311</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Categorizing ancient documents |
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