<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1646-9895</journal-id>
<journal-title><![CDATA[RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação]]></journal-title>
<abbrev-journal-title><![CDATA[RISTI]]></abbrev-journal-title>
<issn>1646-9895</issn>
<publisher>
<publisher-name><![CDATA[AISTI - Associação Ibérica de Sistemas e Tecnologias de Informação]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1646-98952023000100083</article-id>
<article-id pub-id-type="doi">10.17013/risti.49.83-99</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Determinación del mejor algoritmo de detección de matrículas en ambientes controlados y no controlados]]></article-title>
<article-title xml:lang="en"><![CDATA[Determination of the best license plate detection algorithm in controlled and uncontrolled environments.]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Huallpa]]></surname>
<given-names><![CDATA[Elias Ccoto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Macalupu]]></surname>
<given-names><![CDATA[Abel Angel Sullon]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Luque]]></surname>
<given-names><![CDATA[Jorge Eddy Otazu]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Garces]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Peruana Unión Escuela Profesional de Ingeniería de Sistemas ]]></institution>
<addr-line><![CDATA[Puno ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Tecnológica de Lima Sur Facultad de Ingeniería y Gestión ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<numero>49</numero>
<fpage>83</fpage>
<lpage>99</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952023000100083&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952023000100083&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952023000100083&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La seguridad es una prioridad en la gestión y parte de esto es el control y monitoreo para detectar situaciones que agredan el bien público o privado. En este sentido el reconocimiento de matrículas de auto se suma a estos sistemas de monitoreo y control. Este trabajo de investigación aplicó una serie de algoritmos de inteligencia artificial para automatizar dicha detección. En este sentido se utilizó funciones de procesamiento de imágenes con el framework OpenCV considerando que las fuentes de información pudieron tener distitnos escenarios, siendo que el ambiente de detección es abierto y expuesto a las condiciones meteorológicas de la zona. Las tomas de fotos se realizaron en un ambiente ubicado al sur peruano, cuyas condiciones fueron de lluvia, día soleado, día que cayó el granizo. La base de datos de las imágenes entonces se dividió en dos categorías; ambientes controlados donde se consideró una misma distancia, un solo ángulo, pero no necesariamente un mismo clima; y los ambientes no controlados con diferentes ángulos, diferentes distancias y climas. Al procesamiento de imágenes también se utilizó la transformación morfológica, suavizado gaussiano y umbral gaussiano. Con las imágenes segmentadas y extraídos los dígitos de la matrícula; se comparó 3 algoritmos K-NN, SVM y Tesseract. en cada algoritmo se utilizó sus hiperparámetros para el respectivo reconocimiento de caracteres en las imágenes, se obtuvo los siguientes resultados con imágenes tomadas con distintos ángulos y en distintas luminosidades (ambiente no controlado) donde el mejor Overall accuracy con 86 % y el segundo grupo fueron imágenes tomadas con un ángulo similar y distancias similares (ambiente controlado), obtuvo un Overall accuracy de 95.5 %.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Security is a priority in management and part of this is control and monitoring to detect situations that attack the public or private good. In this sense, the recognition of car license plates is added to these monitoring and control systems. This research work applied a series of artificial intelligence algorithms to automate such detection. In this sense, image processing functions were used with the OpenCV framework, considering that the information sources could have different scenarios, since the detection environment is open and exposed to the weather conditions of the area. The photos were taken in an environment located in southern Peru, whose conditions were rainy, sunny, and the day the hail fell. The image database was then divided into two categories; controlled environments where the same distance, a single angle, but not necessarily the same climate was considered; and uncontrolled environments with different angles, different distances and climates. Morphological transformation, Gaussian smoothing and Gaussian thresholding were also used for image processing. With the segmented images and the number plate digits extracted; 3 algorithms K-NN, SVM and Tesseract were compared. In each algorithm, its hyperparameters were used for the respective recognition of characters in the images, the following results were obtained with images taken with different angles and in different luminosities (uncontrolled environment) where the best Overall accuracy with 86% and the second group were images taken with a similar angle and similar distances (controlled environment), obtained an Overall accuracy of 95.5%.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[KNN]]></kwd>
<kwd lng="es"><![CDATA[SVM]]></kwd>
<kwd lng="es"><![CDATA[Tesseract]]></kwd>
<kwd lng="es"><![CDATA[OpenCV]]></kwd>
<kwd lng="es"><![CDATA[Machine Learning y hiperpárametros]]></kwd>
<kwd lng="en"><![CDATA[Tesseract]]></kwd>
<kwd lng="en"><![CDATA[OpenCV]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning and hyperparameters]]></kwd>
</kwd-group>
</article-meta>
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