Need document extraction for multi-page packets

If you need document extraction for multi-page packets, the core problem is usually:

1. Detect packet boundaries
2. Split pages into logical documents
3. Extract key fields from each document
4. Preserve page order and provenance

Typical approach

• Ingest PDFs/scans into image or text form
• Use OCR if pages are scanned
• Detect document separators using:
  • explicit separator pages
  • barcodes / QR codes
  • headers/footers patterns
  • page numbering continuity
  • form templates / layout similarity
• Run classification to label each page/document type
• Use field extraction:
  • rule-based for structured forms
  • ML/LLM-based for semi-structured packets
• Output as:
  • per-document JSON
  • combined packet index
  • extracted text with page references

Common outputs

Example structure:

{
  "packet_id": "PKT-001",
  "documents": [
    {
      "doc_type": "application",
      "pages": [1, 2, 3],
      "fields": {
        "name": "Jane Doe",
        "dob": "1990-01-01"
      }
    },
    {
      "doc_type": "id_card",
      "pages": [4],
      "fields": {
        "id_number": "X1234567"
      }
    }
  ]
}

Best tools / methods

• OCR: Tesseract, AWS Textract, Azure Document Intelligence, Google Document AI
• Layout parsing: LayoutLM, DocTR, PaddleOCR
• Extraction pipelines: custom Python, spaCy, OCR + regex + ML
• For high accuracy on packets: combine page classification + document segmentation + field extraction

Key challenges

• Mixed document types in one packet
• Poor scan quality / skewed pages
• Multi-page forms with repeated headers
• Separators missing or inconsistent
• Handwritten annotations

If you want, I can help with one of these next:

1. a reference architecture for packet extraction,
2. a Python pipeline example,
3. a JSON schema for extracted documents,
4. or a vendor comparison for OCR/document AI platforms.