Anonymize PDF with base64 input
Anonymize a PDF file by detecting and redacting PII (Personally Identifiable Information). Accepts PDF as base64-encoded string in request body. Returns anonymized PDF, detected PII entities, and processing metrics. Supports multiple OCR languages, rotated text detection, and customizable PII tag detection. Only processes the first page of the PDF.
Authorization
APIKeyHeader In: header
Request Body
application/json
Base64-encoded PDF document to be processed
List of predefined PII tags to detect and redact. If empty, all available tags are used
[]Force OCR processing even if text is extractable from PDF
falseList of OCR languages to use for text recognition. Available: ENG, SPA, FRA, DEU, ITA, POR, RUS. Multiple languages can be specified for multilingual documents
["eng"]Enable detection and recognition of rotated text in the document
falseEnable text redaction using NER. 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
Response Body
application/json
application/json
application/json
application/json
curl -X POST "https://api.pdf-redaction.com/api/anonymize/pdf" \ -H "Content-Type: application/json" \ -d '{ "custom_tags": [ "CUSTOM_TAG_1", "CUSTOM_TAG_2" ], "force_ocr": false, "min_chunk_size": 0, "ocr_langs": [ "eng" ], "pdf": "base64_encoded_pdf_string", "redact_text": true, "rotated_text": false, "tags": [ "DATE", "PERSON_NAME", "EMAIL", "PHONE" ] }'{
"pdf": "base64_encoded_pdf_string",
"detected_pii": {
"path": "memory",
"entities": [
{
"entity_group": "PERSON_NAME",
"score": 0.95,
"word": "John Doe",
"start": 0,
"end": 8,
"boxes": [
{
"text": "John Doe",
"score": 0.95,
"x": 100,
"y": 200,
"width": 150,
"height": 25
}
]
},
{
"entity_group": "EMAIL",
"score": 0.98,
"word": "john.doe@example.com",
"start": 0,
"end": 20,
"boxes": [
{
"text": "john.doe@example.com",
"score": 0.98,
"x": 100,
"y": 250,
"width": 200,
"height": 25
}
]
}
],
"exception": "",
"json": ""
},
"processing_time": {
"total": 2.9577243328094482,
"stages": {
"PdfDataToSingleImage": 0.5639204978942871,
"PdfDataToDocument": 0.0037207603454589844,
"Ocr": 0.0004279613494873047,
"Ner": 1.7787754535675049,
"ImageDrawBoxes": 0.5246167182922363,
"SingleImageToPdf": 0.08531355857849121
}
}
}{
"error_code": "LLM_CALL_ERROR",
"message": "string"
}{
"error_code": "LLM_CALL_ERROR",
"message": "string"
}{
"detail": [
{
"loc": [
"string"
],
"msg": "string",
"type": "string"
}
]
}