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POST
/
api
/
v1
/
ocr
/
preview
Process Document with OCR
curl --request POST \
  --url https://api.example.com/api/v1/ocr/preview \
  --header 'Content-Type: application/json' \
  --data '{}'
{
  "text": "<string>",
  "error": "<string>"
}

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/LuisCastilloCruz/VIGIA/llms.txt

Use this file to discover all available pages before exploring further.

​
Overview

This endpoint processes uploaded documents (PDFs and images) and extracts text using OCR technology powered by Tesseract. The service automatically detects the document type and applies appropriate processing methods.

​
Supported File Formats

  • Images: PNG, JPG, JPEG, WEBP, TIF, TIFF
  • Documents: PDF (multi-page support)

​
Request

​
file
file
required
The document or image file to process. Must be one of the supported formats.

​
Example Request

curl -X POST https://api.vigia.com/api/v1/ocr/preview \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@document.pdf"

import requests

url = "https://api.vigia.com/api/v1/ocr/preview"
files = {"file": open("document.pdf", "rb")}
headers = {"Authorization": "Bearer YOUR_API_KEY"}

response = requests.post(url, files=files, headers=headers)
print(response.json())

​
Response

​
text
string
The extracted text from the document. For multi-page PDFs, text from all pages is concatenated with newline separators.

​
Example Response

{
  "text": "Paciente: C.R.\nEdad: 45 años\nSexo: Masculino\nPeso: 72 kg\n\nProducto sospechoso: Ibuprofeno 400 mg\nFecha de inicio: 2025-08-10\n\nEvento adverso: Presentó urticaria generalizada tras administración del medicamento.\n\nReportante: Dra. María González"
}

​
OCR Processing Details

​
Image Processing

For image files, the service:
  1. Loads the image using OpenCV (if available) or PIL
  2. Converts to grayscale
  3. Applies Otsu’s thresholding for better text detection
  4. Runs Tesseract OCR with Spanish and English language support

​
PDF Processing

For PDF files, the service uses two strategies: Primary Method: pdf2image + Poppler
  • Converts each PDF page to high-resolution images
  • Applies OCR to each page individually
  • Concatenates results from all pages
Fallback Method: PyMuPDF (fitz)
  • Rasterizes PDF pages at 200 DPI
  • Processes each page with Tesseract
  • Used when Poppler is not available

​
Language Support

The OCR engine attempts text extraction in the following order:
  1. Spanish (spa)
  2. Spanish + English (spa+eng)
  3. English only (eng)
  4. Default language (fallback)

​
Configuration

OCR behavior is configured via environment variables:
  • TESSERACT_CMD: Path to Tesseract executable
  • POPPLER_PATH: Path to Poppler utilities (for PDF processing)
  • OCR_CONFIG: Tesseract configuration (default: --oem 3 --psm 6)

​
Debug Endpoint

To check OCR service configuration and available languages: 
GET /api/v1/ocr/debug
Returns: 
{
  "TESSERACT_CMD_env": "/usr/bin/tesseract",
  "pytesseract_cmd": "/usr/bin/tesseract",
  "tesseract_version": "tesseract 5.3.0",
  "langs": ["eng", "spa", "spa_old"],
  "POPPLER_PATH": "/usr/bin"
}

​
Error Handling

​
error
string
Error message if OCR processing fails

​
Common Errors

  • Unsupported file format: The uploaded file type is not supported
  • OCR disabled: Tesseract is not properly installed or configured
  • PDF processing failed: Poppler/PyMuPDF dependencies missing
  • Invalid file: The uploaded file is corrupted or cannot be read

​
Best Practices

  1. Image Quality: Upload high-resolution images (300 DPI minimum) for best results
  2. File Size: Keep files under 10 MB for optimal processing time
  3. Document Orientation: Ensure text is properly oriented (not rotated)
  4. Contrast: High contrast between text and background improves accuracy
  5. Multi-page PDFs: Processing time increases linearly with page count

​
Performance

  • Single page: ~2-3 seconds
  • Multi-page PDF: ~2-3 seconds per page
  • Large images: ~3-5 seconds depending on resolution

​
Next Steps

After extracting text with OCR, you can:
  • Use Extract Data to parse structured fields from the text
  • Use Translate to translate the extracted text to other languages

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