Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina

La extracción automática de información de documentos de identidad es una tarea fundamental en diferentes procesos digitales como registros, solicitud de productos, validación de identidad, entre otros. La extracción de información consiste en la identificación, ubicación, clasificación y reconocimi...

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Autores:: Márquez Aristizábal, Hugo Alejandro

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
dc.title.translated.eng.fl_str_mv	Information extraction from identification documents using machine learning techniques
title	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
spellingShingle	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Identidad digital Reconocimiento óptico de caracteres Redes neuronales (Computadoers) Ientidad digital OCR Extracción de información Detección de objetos Digital identity Information extraction Object detection
title_short	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
title_full	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
title_fullStr	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
title_full_unstemmed	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
title_sort	Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina
dc.creator.fl_str_mv	Márquez Aristizábal, Hugo Alejandro
dc.contributor.advisor.none.fl_str_mv	Villa Garzón, Fernán Alonso
dc.contributor.author.none.fl_str_mv	Márquez Aristizábal, Hugo Alejandro
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::003 - Sistemas
topic	000 - Ciencias de la computación, información y obras generales::003 - Sistemas Identidad digital Reconocimiento óptico de caracteres Redes neuronales (Computadoers) Ientidad digital OCR Extracción de información Detección de objetos Digital identity Information extraction Object detection
dc.subject.other.none.fl_str_mv	Identidad digital Reconocimiento óptico de caracteres
dc.subject.lemb.none.fl_str_mv	Redes neuronales (Computadoers)
dc.subject.proposal.spa.fl_str_mv	Ientidad digital OCR Extracción de información Detección de objetos
dc.subject.proposal.eng.fl_str_mv	Digital identity Information extraction Object detection
description	La extracción automática de información de documentos de identidad es una tarea fundamental en diferentes procesos digitales como registros, solicitud de productos, validación de identidad, entre otros. La extracción de información consiste en la identificación, ubicación, clasificación y reconocimiento del texto de campos clave presentes en un documento, en este caso un documento de identidad. Tratándose de documentos de identidad, los campos clave son aquellos como: nombres, apellidos, números de documento, fechas, entre otros. El problema de extracción de información se ha solucionado tradicionalmente utilizando algoritmos basados en reglas y motores clásicos de OCR. En los últimos años se han realizado implementaciones de modelos de aprendizaje de máquina, utilizando modelos de NLP (procesamiento de lenguaje natural) y CV (visión por computador) para solucionar el problema de una manera más flexible y eficiente (Subramani et al., 2020). En este trabajo se propuso solucionar el problema de extracción de información con una aproximación de detección de objetos. Se implementó, entrenó y evaluó un modelo de detección de objetos basado en transformadores (Carion et al., 2020). Se logró llegar a una solución que alcanza valores de precisión superiores al 95% en la detección de campos clave en documentos de identidad. (Texto tomado de la fuente)
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-08-22T21:32:48Z
dc.date.available.none.fl_str_mv	2022-08-22T21:32:48Z
dc.date.issued.none.fl_str_mv	2022-06-23
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82000
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82000 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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dc.rights.license.spa.fl_str_mv	Reconocimiento 4.0 Internacional
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dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
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dc.format.extent.spa.fl_str_mv	67 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Villa Garzón, Fernán Alonso9c83ea56495b8f17a79c27fd0001bb81600Márquez Aristizábal, Hugo Alejandro7d8af31e6325c06bdfdd23246aa215802022-08-22T21:32:48Z2022-08-22T21:32:48Z2022-06-23https://repositorio.unal.edu.co/handle/unal/82000Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/La extracción automática de información de documentos de identidad es una tarea fundamental en diferentes procesos digitales como registros, solicitud de productos, validación de identidad, entre otros. La extracción de información consiste en la identificación, ubicación, clasificación y reconocimiento del texto de campos clave presentes en un documento, en este caso un documento de identidad. Tratándose de documentos de identidad, los campos clave son aquellos como: nombres, apellidos, números de documento, fechas, entre otros. El problema de extracción de información se ha solucionado tradicionalmente utilizando algoritmos basados en reglas y motores clásicos de OCR. En los últimos años se han realizado implementaciones de modelos de aprendizaje de máquina, utilizando modelos de NLP (procesamiento de lenguaje natural) y CV (visión por computador) para solucionar el problema de una manera más flexible y eficiente (Subramani et al., 2020). En este trabajo se propuso solucionar el problema de extracción de información con una aproximación de detección de objetos. Se implementó, entrenó y evaluó un modelo de detección de objetos basado en transformadores (Carion et al., 2020). Se logró llegar a una solución que alcanza valores de precisión superiores al 95% en la detección de campos clave en documentos de identidad. (Texto tomado de la fuente)Automatic information extraction from identity documents is a fundamental task in digital processes such as onboarding, requesting products, identity validation, among others. The information extraction process consists of identifying, locating, classifying and recognizing text of the corresponding key fields that an identity document contains. In the case of identity documents, key fields are: names, last names, document number, dates, among others. The information extraction problem has been traditionally solved using rule based algorithms and classic OCR engines. In the last few years there have been implementations based on machine learning models, using NLP (natural language processing) and CV (computer vision) to solve the problem in a more flexible and efficient way (Subramani et al., 2020). This work proposes to solve the problem of information extraction with an object detection approach. An object detection model based on transformers (Carion et al., 2020) was implemented, trained and evaluated. A solution with above 95% accuracy in detecting key fields on identification documents was achieved.MaestríaMagíster en Ingeniería - AnalíticaÁrea Curricular de Ingeniería de Sistemas e Informática67 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaDepartamento de la Computación y la DecisiónFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - SistemasIdentidad digitalReconocimiento óptico de caracteresRedes neuronales (Computadoers)Ientidad digitalOCRExtracción de informaciónDetección de objetosDigital identityInformation extractionObject detectionExtracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquinaInformation extraction from identification documents using machine learning techniquesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAl-Badr, B., & Mahmoud, S. 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Object Detection in 20 Years: A Survey. 40.EstudiantesInvestigadoresORIGINAL1017231914.2022.pdf1017231914.2022.pdfapplication/pdf7517510https://repositorio.unal.edu.co/bitstream/unal/82000/1/1017231914.2022.pdf158554034e1cafc545f3e08c65c9e66dMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/82000/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1017231914.2022.pdf.jpg1017231914.2022.pdf.jpgGenerated Thumbnailimage/jpeg4276https://repositorio.unal.edu.co/bitstream/unal/82000/3/1017231914.2022.pdf.jpg2e11d994dbf59e37fa0a57813b5f1fd3MD53unal/82000oai:repositorio.unal.edu.co:unal/820002023-10-06 17:38:53.423Repositorio Institucional Universidad Nacional de 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Extracción de información de documentos de identidad utilizando técnicas de aprendizaje de máquina

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