I'm building a document ingestion pipeline and need OCR with structured JSON output

A solid OCR pipeline for document ingestion usually has 4 stages:

1. Document upload & normalization

   • Accept PDF, TIFF, PNG, JPG
   • Split multi-page PDFs into pages
   • Deskew, denoise, rotate, and enhance contrast

2. OCR + layout extraction

   • Extract:
     • full text
     • page number
     • word-level boxes
     • line/paragraph blocks
     • tables, key-value pairs, checkboxes if needed
   • Common engine options:
     • Cloud: AWS Textract, Google Document AI, Azure Form Recognizer
     • Open source: Tesseract, PaddleOCR, EasyOCR, docTR

3. Structured JSON normalization

   • Convert OCR output into a consistent schema, for example:

     {
       "document_id": "doc_123",
       "pages": [
         {
           "page_number": 1,
           "width": 2550,
           "height": 3300,
           "blocks": [
             {
               "type": "paragraph",
               "text": "Invoice #12345",
               "bbox": [100, 120, 300, 160],
               "confidence": 0.98
             }
           ]
         }
       ],
       "metadata": {
         "source_file": "invoice.pdf",
         "language": "en"
       }
     }
     

   • Keep bounding boxes in normalized coordinates if possible
   • Preserve confidence scores and reading order

4. Post-processing / extraction

   • Use rules or ML to extract entities like:
     • invoice number
     • dates
     • totals
     • names/addresses
   • Validate fields with regex and business rules

Recommended JSON schema

If you want a practical ingestion format, use something like:

{
  "document_id": "string",
  "file_name": "string",
  "mime_type": "string",
  "pages": [
    {
      "page_number": 1,
      "dimensions": {
        "width": 0,
        "height": 0
      },
      "text": "full page text",
      "blocks": [
        {
          "id": "block_1",
          "type": "text|table|key_value|checkbox|figure",
          "text": "extracted text",
          "confidence": 0.0,
          "bbox": [0, 0, 0, 0],
          "children": []
        }
      ]
    }
  ],
  "entities": {
    "invoice_number": "INV-123",
    "date": "2026-01-01",
    "total": 123.45
  },
  "processing": {
    "ocr_engine": "textract",
    "language": ["en"],
    "created_at": "timestamp"
  }
}

If you want the easiest production path

• Best managed option: AWS Textract or Google Document AI
• Best self-hosted option: PaddleOCR + layout parser + your own JSON mapper
• Best for forms/invoices: Textract / Document AI because they already return structured elements

Good design tips

• Store both:
  • raw OCR output
  • normalized canonical JSON
• Version your schema
• Keep original coordinates and source page references
• Include confidence and provenance for every extracted field
• Make the OCR stage idempotent so documents can be reprocessed safely

If you want, I can also provide:

1. a Python reference architecture,
2. a JSON schema (Draft 2020-12), or
3. a sample OCR-to-JSON mapping implementation.