Colombian Sign Language Interpretation Model using Artificial Intelligence
In this work, two interpretation models of Colombian Sign Language (CSL) are presented, using static and dynamic methods that employ artificial intelligence. The CRISP-DM methodology was used as a reference, creating a database with videos from seventy non-expert participants, being preprocessed and...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840 |
| _version_ | 1801706013315825664 |
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| author | Muñoz-Galindez, Jader Alejandro Vargas-Cañas, Rubiel |
| author_facet | Muñoz-Galindez, Jader Alejandro Vargas-Cañas, Rubiel |
| author_sort | Muñoz-Galindez, Jader Alejandro |
| collection | OJS |
| description | In this work, two interpretation models of Colombian Sign Language (CSL) are presented, using static and dynamic methods that employ artificial intelligence. The CRISP-DM methodology was used as a reference, creating a database with videos from seventy non-expert participants, being preprocessed and subsequently divided into proportions of 70% - 30% for training and testing, respectively. The repository was named LSC-W70 and was used on a pre-trained model of convolutional neural networks and another in combination with LSTM networks. The results reached 67% and 76% accuracy for the static and dynamic models, respectively, where the dynamic model presents improvements in similar signs by identifying the direction of movement to define the type of sign. In this sense, a dynamic Colombian sign language interpretation tool was developed that helps close communication gaps, generating equality between people. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-16840 |
| institution | Revista de Investigación, Desarrollo e Innovación (RIDI) |
| language | spa |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-168402024-05-30T03:55:49Z Colombian Sign Language Interpretation Model using Artificial Intelligence Modelo de interpretación de lengua de señas colombiano usando inteligencia artificial Muñoz-Galindez, Jader Alejandro Vargas-Cañas, Rubiel colombian sign language; CNN; LSTM; CRISP-DM lengua de señas colombiano; CNN; LSTM; CRISP-DM In this work, two interpretation models of Colombian Sign Language (CSL) are presented, using static and dynamic methods that employ artificial intelligence. The CRISP-DM methodology was used as a reference, creating a database with videos from seventy non-expert participants, being preprocessed and subsequently divided into proportions of 70% - 30% for training and testing, respectively. The repository was named LSC-W70 and was used on a pre-trained model of convolutional neural networks and another in combination with LSTM networks. The results reached 67% and 76% accuracy for the static and dynamic models, respectively, where the dynamic model presents improvements in similar signs by identifying the direction of movement to define the type of sign. In this sense, a dynamic Colombian sign language interpretation tool was developed that helps close communication gaps, generating equality between people. En este trabajo se presentan dos modelos de interpretación de Lengua de Señas Colombiana (LSC), usando métodos estáticos y dinámicos que emplean inteligencia artificial. Se utilizó como referente la metodología CRISP-DM, creando una base de datos con videos de setenta participantes no expertos, siendo preprocesados y posteriormente divididos en proporciones de 70% - 30% para entrenamiento y prueba, respectivamente. El repositorio se nombró como LSC-W70 y se empleó sobre un modelo preentrenado de redes neuronales convolucionales y otro en combinación con redes LSTM. Los resultados alcanzaron un 67% y 76% accuracy para los modelos estático y dinámico, respectivamente, donde el modelo dinámico presenta mejoras en señas similares identificando la dirección del movimiento para definir el tipo de seña. En este sentido, se desarrolló una herramienta de interpretación dinámica de lengua de señas colombiano que ayuda a cerrar brechas de comunicación generando igualdad entre las personas. Universidad Pedagógica y Tecnológica de Colombia 2023-08-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840 10.19053/20278306.v13.n2.2023.16840 Revista de Investigación, Desarrollo e Innovación; Vol. 13 No. 2 (2023): Julio-Diciembre; 357-366 Revista de Investigación, Desarrollo e Innovación; Vol. 13 Núm. 2 (2023): Julio-Diciembre; 357-366 2389-9417 2027-8306 spa https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840/13652 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840/13935 Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovación |
| spellingShingle | colombian sign language; CNN; LSTM; CRISP-DM lengua de señas colombiano; CNN; LSTM; CRISP-DM Muñoz-Galindez, Jader Alejandro Vargas-Cañas, Rubiel Colombian Sign Language Interpretation Model using Artificial Intelligence |
| title | Colombian Sign Language Interpretation Model using Artificial Intelligence |
| title_alt | Modelo de interpretación de lengua de señas colombiano usando inteligencia artificial |
| title_full | Colombian Sign Language Interpretation Model using Artificial Intelligence |
| title_fullStr | Colombian Sign Language Interpretation Model using Artificial Intelligence |
| title_full_unstemmed | Colombian Sign Language Interpretation Model using Artificial Intelligence |
| title_short | Colombian Sign Language Interpretation Model using Artificial Intelligence |
| title_sort | colombian sign language interpretation model using artificial intelligence |
| topic | colombian sign language; CNN; LSTM; CRISP-DM lengua de señas colombiano; CNN; LSTM; CRISP-DM |
| topic_facet | colombian sign language; CNN; LSTM; CRISP-DM lengua de señas colombiano; CNN; LSTM; CRISP-DM |
| url | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16840 |
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