Anonimizar PDF con carga de archivo
Anonimice un archivo PDF detectando y redactando PII (Información de Identificación Personal). Acepta PDF como carga de archivo multipart/form-data. Devuelve PDF anonimizado como flujo binario. Este endpoint es útil para cargas directas de archivos sin codificación base64. Admite múltiples idiomas OCR, detección de texto rotado y detección personalizable de etiquetas PII mediante parámetros de consulta. Solo procesa la primera página del PDF.
Authorization
APIKeyHeader In: header
Request Body
multipart/form-data
PDF file to anonymize
binaryForce OCR processing even if text is extractable from PDF
falseEnable detection and recognition of rotated text in the document
falseEnable text redaction using NER (Named Entity Recognition). When enabled, detected PII entities are redacted (blacked out) in the output PDF
trueMinimum chunk size for text processing. Used to control text segmentation for NER processing. Larger values may improve accuracy but increase processing time
0List of custom tags to detect and redact. These tags are added to the standard PII tags
List of predefined PII tags to detect and redact. If empty, all available tags are used. Available tags: DATE, PERSON_NAME, ORGANIZATION, LOCATION, EMAIL, PHONE, ID, ACCOUNT, ZIP_CODE, ADDRESS, IP, URL, SSN, DRIVER_LICENSE, PASSPORT, PASSWORD, AGE, CREDIT_CARD, MONEY_AMOUNT, SIGNATURE, QR_CODE, FACE. Can be comma-separated string like 'PERSON_NAME,EMAIL,PHONE' or list of strings.
List of OCR languages to use for text recognition. Available languages: eng (English), spa (Spanish), fra (French), deu (German), ita (Italian), por (Portuguese), rus (Russian). Defaults to English only. Multiple languages can improve accuracy for multilingual documents. Can be comma-separated string like 'eng,spa' or list of strings.
["eng"]Response Body
application/pdf
application/json
application/json
application/json
curl -X POST "https://api.pdf-redaction.com/api/anonymize/file/pdf" \ -F pdf="document.pdf""binary pdf data"{
"detail": "Invalid document type"
}{
"error_code": "LLM_CALL_ERROR",
"message": "string"
}{
"detail": [
{
"loc": [
"string"
],
"msg": "string",
"type": "string"
}
]
}