Manuscript Character Recognition
- Autores
- De Giusti, Marisa Raquel; Villarreal, Gonzalo Luján; Vila, María Marta
- Año de publicación
- 2006
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión enviada
- Descripción
- The image recognizing process requires the identification of every logical object that compose every image, which first implies to recognize it as an object (segmentation) and then identify which object is, or at least which is the most likely one from the universe of objects that can be recognized (recognition). During the segmentation process, the aim is to identify as many objects that compose the images as possible. This process must be adapted to the universe of all objects that are looked for, which can vary from printed or manuscript characters to fruits or animals, or even fingerprints. Once all objects have been obtained, the system must carry on to the next step, which is the identification of the objects based on the called universe. In other words, if the system is looking for fruits, it must identify univocally fruits from apples and oranges; if they are characters, it must identify the character a from the rest of the alphabet, and soon. In this document, the character recognition step has been studied. More specifically, which methods to obtain characteristics exist (advantages and disadvantages, implementations, costs). There is also an overview about the feature vector, in which all features are stored and analyzed in order to perform the character recognition itself.
- Materia
-
Ciencias de la Computación e Información
Image processing software - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/3825
Ver los metadatos del registro completo
id |
CICBA_7766a61b77a611796e90e4037503a67e |
---|---|
oai_identifier_str |
oai:digital.cic.gba.gob.ar:11746/3825 |
network_acronym_str |
CICBA |
repository_id_str |
9441 |
network_name_str |
CIC Digital (CICBA) |
spelling |
Manuscript Character RecognitionDe Giusti, Marisa RaquelVillarreal, Gonzalo LujánVila, María MartaCiencias de la Computación e InformaciónImage processing softwareThe image recognizing process requires the identification of every logical object that compose every image, which first implies to recognize it as an object (segmentation) and then identify which object is, or at least which is the most likely one from the universe of objects that can be recognized (recognition). During the segmentation process, the aim is to identify as many objects that compose the images as possible. This process must be adapted to the universe of all objects that are looked for, which can vary from printed or manuscript characters to fruits or animals, or even fingerprints. Once all objects have been obtained, the system must carry on to the next step, which is the identification of the objects based on the called universe. In other words, if the system is looking for fruits, it must identify univocally fruits from apples and oranges; if they are characters, it must identify the character a from the rest of the alphabet, and soon. In this document, the character recognition step has been studied. More specifically, which methods to obtain characteristics exist (advantages and disadvantages, implementations, costs). There is also an overview about the feature vector, in which all features are stored and analyzed in order to perform the character recognition itself.2006-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/3825enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:15Zoai:digital.cic.gba.gob.ar:11746/3825Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:15.778CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
dc.title.none.fl_str_mv |
Manuscript Character Recognition |
title |
Manuscript Character Recognition |
spellingShingle |
Manuscript Character Recognition De Giusti, Marisa Raquel Ciencias de la Computación e Información Image processing software |
title_short |
Manuscript Character Recognition |
title_full |
Manuscript Character Recognition |
title_fullStr |
Manuscript Character Recognition |
title_full_unstemmed |
Manuscript Character Recognition |
title_sort |
Manuscript Character Recognition |
dc.creator.none.fl_str_mv |
De Giusti, Marisa Raquel Villarreal, Gonzalo Luján Vila, María Marta |
author |
De Giusti, Marisa Raquel |
author_facet |
De Giusti, Marisa Raquel Villarreal, Gonzalo Luján Vila, María Marta |
author_role |
author |
author2 |
Villarreal, Gonzalo Luján Vila, María Marta |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias de la Computación e Información Image processing software |
topic |
Ciencias de la Computación e Información Image processing software |
dc.description.none.fl_txt_mv |
The image recognizing process requires the identification of every logical object that compose every image, which first implies to recognize it as an object (segmentation) and then identify which object is, or at least which is the most likely one from the universe of objects that can be recognized (recognition). During the segmentation process, the aim is to identify as many objects that compose the images as possible. This process must be adapted to the universe of all objects that are looked for, which can vary from printed or manuscript characters to fruits or animals, or even fingerprints. Once all objects have been obtained, the system must carry on to the next step, which is the identification of the objects based on the called universe. In other words, if the system is looking for fruits, it must identify univocally fruits from apples and oranges; if they are characters, it must identify the character a from the rest of the alphabet, and soon. In this document, the character recognition step has been studied. More specifically, which methods to obtain characteristics exist (advantages and disadvantages, implementations, costs). There is also an overview about the feature vector, in which all features are stored and analyzed in order to perform the character recognition itself. |
description |
The image recognizing process requires the identification of every logical object that compose every image, which first implies to recognize it as an object (segmentation) and then identify which object is, or at least which is the most likely one from the universe of objects that can be recognized (recognition). During the segmentation process, the aim is to identify as many objects that compose the images as possible. This process must be adapted to the universe of all objects that are looked for, which can vary from printed or manuscript characters to fruits or animals, or even fingerprints. Once all objects have been obtained, the system must carry on to the next step, which is the identification of the objects based on the called universe. In other words, if the system is looking for fruits, it must identify univocally fruits from apples and oranges; if they are characters, it must identify the character a from the rest of the alphabet, and soon. In this document, the character recognition step has been studied. More specifically, which methods to obtain characteristics exist (advantages and disadvantages, implementations, costs). There is also an overview about the feature vector, in which all features are stored and analyzed in order to perform the character recognition itself. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-10-01 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
submittedVersion |
dc.identifier.none.fl_str_mv |
https://digital.cic.gba.gob.ar/handle/11746/3825 |
url |
https://digital.cic.gba.gob.ar/handle/11746/3825 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
reponame_str |
CIC Digital (CICBA) |
collection |
CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
instacron_str |
CICBA |
institution |
CICBA |
repository.name.fl_str_mv |
CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
repository.mail.fl_str_mv |
marisa.degiusti@sedici.unlp.edu.ar |
_version_ |
1844618612414873600 |
score |
13.069144 |