Revisión sistemática de técnicas basadas en IA para la extracción de información de documentos no estructurados
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En la actualidad, muchas organizaciones gestionan o conservan sus documentos físicos en formato digital, guardando la información en archivos no estructurados como los PDF, sin embargo, al tratarse de documentos escaneados, la extracción y el análisis de la información resultan difíciles de obtener. Cuando se utilizan de forma adecuada, estos datos pueden contribuir significativamente a mejorar la eficiencia operativa. Aunque los métodos tradicionales de extracción de información presentan ciertas limitaciones, las soluciones basadas en inteligencia artificial (IA) se perfilan como una alternativa más eficaz. No obstante, persiste una clara falta de estudios académicos que ofrezcan una evaluación exhaustiva de las técnicas de IA aplicadas a la extracción de información en contenidos no estructurados. Esta Revisión Sistemática de la Literatura se lleva a cabo con la metodología (PRISMA) que tiene como objetivo identificar y examinar los estudios existentes relacionados con las técnicas aplicadas a la extracción de texto a partir de documentos no estructurados, así como ofrecer orientaciones que puedan guiar investigaciones futuras en este ámbito. A través de un análisis detallado de 40 estudios seleccionados publicados entre 2020 a 2025 se identifican los principales modelos que sirven para la extracción de texto de documentos. La revisión revela que las técnicas impulsadas por IA tienen un gran potencial para permitir la extracción automática de información en documentos no estructurados, ya sean impresos o manuscritos. No obstante, trabajar con formatos variados de documentos plantea ciertos desafíos. Como respuesta, se propone un marco basado en enfoques híbridos de IA, orientado al procesamiento eficaz de conjuntos de datos de alta calidad para facilitar dicha extracción. Asimismo, se destaca la necesidad de una colaboración activa entre organizaciones e investigadores, clave para enfrentar las múltiples complejidades que implica el análisis de datos no estructurados.
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