Docs aligment on latest metrics.
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@@ -69,7 +69,7 @@ El objetivo principal del trabajo era alcanzar un CER inferior al 2% en document
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3. **Ground truth automático**: La extracción automática del texto de referencia puede introducir errores en layouts complejos.
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4. **Ejecución en CPU**: Los tiempos de procesamiento (~69s/página) limitan la aplicabilidad en escenarios de alto volumen.
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4. **Validación en entorno limitado**: Aunque se validó con GPU (126x más rápido que CPU, 0.55s/página), los experimentos se realizaron en hardware de consumo (RTX 3060). Hardware empresarial podría ofrecer mejor rendimiento.
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5. **Parámetro no explorado**: `text_det_unclip_ratio` permaneció fijo en 0.0 durante todo el experimento.
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@@ -83,8 +83,6 @@ El objetivo principal del trabajo era alcanzar un CER inferior al 2% en document
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3. **Dataset ampliado**: Construir un corpus más amplio y diverso de documentos en español.
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4. **Evaluación con GPU**: Medir tiempos de inferencia con aceleración GPU.
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### Líneas de Investigación
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1. **Transfer learning de hiperparámetros**: Investigar si las configuraciones óptimas para un tipo de documento transfieren a otros dominios.
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