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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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100 |
1 |
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|a Marinai, Simone
|e verfasserin
|4 aut
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1 |
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|a Artificial neural networks for document analysis and recognition
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|c 2005
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 01.02.2005
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment
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|a Comparative Study
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|a Evaluation Study
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|a Journal Article
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|a Validation Study
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|a Gori, Marco
|e verfasserin
|4 aut
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|a Soda, Giovanni
|e verfasserin
|4 aut
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1 |
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|a Society, Computer
|e verfasserin
|4 aut
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773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 27(2005), 1 vom: 10. Jan., Seite 23-35
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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773 |
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|g volume:27
|g year:2005
|g number:1
|g day:10
|g month:01
|g pages:23-35
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|d 27
|j 2005
|e 1
|b 10
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|h 23-35
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