La API de OCR para Extraer Texto de Imágenes te permite convertir imágenes que contienen texto en datos estructurados que pueden ser procesados y analizados digitalmente. Su función principal es identificar automáticamente palabras, líneas y bloques de texto dentro de una imagen, proporcionando no solo el contenido textual, sino también información sobre la ubicación exacta de cada elemento dentro de la imagen utilizando coordenadas de cuadro delimitador. Esto facilita tareas como la extracción de datos de documentos, pasaportes, facturas, formularios o cualquier imagen que contenga texto.
Cada palabra reconocida por la API incluye un valor de confianza que indica la probabilidad de que el reconocimiento sea correcto, lo que te permite filtrar o revisar los resultados según su precisión. La API organiza la información de manera jerárquica: los textos se agrupan en bloques, los bloques contienen párrafos, y los párrafos contienen líneas y palabras individuales. Esta estructura facilita el análisis de documentos complejos y mantiene el contexto del texto extraído.
Además de la transcripción textual, la API puede capturar información de formato como puntuación, uso de mayúsculas y separaciones de palabras, y puede proporcionar metadatos útiles para el procesamiento de documentos, búsqueda y aplicaciones de análisis automatizado. La salida incluye coordenadas normalizadas (valores entre 0 y 1) que representan la posición del texto en la imagen, lo que permite la reconstrucción visual del contenido o la integración con sistemas de marcado y anotación.
La API es particularmente útil en escenarios donde se necesita digitalizar documentos físicos o escaneados, automatizar procesos de entrada de datos o construir sistemas de lectura de documentos para auditoría, control de identidad o gestión de documentos. Su enfoque modular y detallado permite tanto la rápida extracción de texto como análisis más profundos, incluyendo la validación de datos sensibles como nombres, números de identificación y fechas, como se ve en un ejemplo de reconocimiento de pasaporte haitiano, donde se extraen nombres, fechas y códigos de manera jerárquica y detallada.
En resumen, esta API combina el reconocimiento óptico de caracteres, la precisión en la ubicación de cada palabra y la estructura jerárquica para convertir imágenes en datos textuales confiables y accionables.
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Seer FEMININ Gat peeps » set Dots Senissinn SIVAT) MET PASPO 4 17 DECEMBRE 1992 MSIGNGTURE Ou TITULAIRE amt parno 3 tn Dots Pespirotca 16 DECEMBRE 1997 920000018 SPECIMEN","boxCoordinates":[0.1197110423116615,0.05071315372424723,0.8421052631578947,0.8557844690966719],"blocks":[{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":": AYITI: am","words":[{"text":":","boxCoordinates":[0.20227038183694532,0.08240887480190175,0.0030959752321981426,0.003169572107765452],"confidence":0.0},{"text":"AYITI:","boxCoordinates":[0.6945304437564499,0.05071315372424723,0.0608875128998968,0.06497622820919176],"confidence":0.0},{"text":"am","boxCoordinates":[0.762641898864809,0.06656101426307448,0.048503611971104234,0.07131537242472266],"confidence":46.0}],"boxCoordinates":[0.20227038183694532,0.05071315372424723,0.608875128998968,0.08716323296354993]},{"text":"PASPO re Py asia Etat","words":[{"text":"PASPO","boxCoordinates":[0.15067079463364294,0.08557844690966719,0.06398348813209494,0.05705229793977813],"confidence":87.0},{"text":"re","boxCoordinates":[0.5479876160990712,0.11727416798732171,0.022703818369453045,0.017432646592709985],"confidence":21.0},{"text":"Py","boxCoordinates":[0.5851393188854489,0.12519809825673534,0.009287925696594427,0.030110935023771792],"confidence":46.0},{"text":"asia","boxCoordinates":[0.608875128998968,0.10935023771790808,0.04953560371517028,0.05229793977812995],"confidence":32.0},{"text":"Etat","boxCoordinates":[0.6656346749226006,0.10935023771790808,0.034055727554179564,0.039619651347068144],"confidence":13.0}],"boxCoordinates":[0.15067079463364294,0.08557844690966719,0.5490196078431373,0.07606973058637084]},{"text":"oa ta","words":[{"text":"oa","boxCoordinates":[0.5954592363261094,0.11410459587955626,0.02476780185758514,0.06497622820919176],"confidence":25.0},{"text":"ta","boxCoordinates":[0.6336429308565531,0.14580031695721077,0.02476780185758514,0.01901743264659271],"confidence":29.0}],"boxCoordinates":[0.5954592363261094,0.11410459587955626,0.0629514963880289,0.06497622820919176]}],"boxCoordinates":[0.15067079463364294,0.05071315372424723,0.6604747162022704,0.12836767036450078]}],"boxCoordinates":[0.15067079463364294,0.05071315372424723,0.6604747162022704,0.12836767036450078]},{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"PASSEPORT Aalto! 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curl --location --request POST 'https://zylalabs.com/api/11265/extract+text+from+images+ocr+api/21263/text+extraction?image_url=https://static-content.regulaforensics.com/Hardware-products/knowledge_hub/glossary_documents/PASSPORT/2l.webp' --header 'Authorization: Bearer YOUR_API_KEY'
| Encabezado | Descripción |
|---|---|
Autorización
|
[Requerido] Debería ser Bearer access_key. Consulta "Tu Clave de Acceso a la API" arriba cuando estés suscrito. |
Sin compromiso a largo plazo. Mejora, reduce o cancela en cualquier momento. La Prueba Gratuita incluye hasta 50 solicitudes.
El punto final de extracción de texto devuelve datos estructurados que incluyen texto reconocido, coordenadas de la caja delimitadora para cada palabra, línea y bloque, puntajes de confianza que indican la precisión del reconocimiento y una organización jerárquica del texto (bloques, párrafos, líneas, palabras)
Los campos clave en los datos de respuesta incluyen "texto" (el contenido reconocido) "coordenadas" (posiciones de los cuadros delimitadores) "confianza" (puntuación de exactitud) y "jerarquía" (estructura que indica bloques párrafos líneas y palabras)
Los datos de respuesta están organizados jerárquicamente, con bloques que contienen párrafos, párrafos que contienen líneas y líneas que contienen palabras individuales. Esta estructura permite una fácil navegación y análisis del texto extraído
El punto final proporciona información como texto reconocido su ubicación dentro de la imagen niveles de confianza para cada reconocimiento y detalles de formato como puntuación y capitalización lo que lo hace adecuado para varios tipos de documentos
Los usuarios pueden personalizar sus solicitudes especificando parámetros como el formato de imagen las configuraciones de idioma y la estructura de salida deseada lo que permite una extracción personalizada basada en tipos de documentos o requisitos específicos
La precisión de los datos se mantiene a través de algoritmos avanzados de reconocimiento óptico de caracteres que incluyen puntuación de confianza para cada elemento reconocido permitiendo a los usuarios filtrar resultados según su fiabilidad
Los casos de uso típicos incluyen digitalizar documentos escaneados automatizar la entrada de datos de formularios o facturas y construir sistemas de lectura de documentos para verificación de identidad o fines de auditoría
Los usuarios deben verificar los puntajes de confianza en la respuesta; puntajes bajos pueden indicar resultados parciales o inexactos. Implementar un proceso de revisión para entradas de baja confianza puede ayudar a asegurar la calidad y completitud de los datos
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