Page Classification enables you to automatically categorize pages within documents based on their content. This allows you to label pages to filter them for downstream use-cases like structured extraction and OCR.
Try this out using this Colab Notebook.

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How Page Classification works

Page Classifications work by analyzing each page of a document and assigning it to one or more predefined categories that you specify. When you initiate a parse job, you can provide a list of page classification configurations as part of your request. Each page classification configuration consists of:
  • Name: A unique identifier for the page class
  • Description: A detailed description that guides the AI model in identifying pages that belong to this category
If you have specified page classes in your parse request, Tensorlake analyzes each page of the document and assigns it to one or more categories that you specify.

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Classification Example

import time
from tensorlake.documentai import (
    DocumentAI,
    PageClassConfig,
)

doc_ai = DocumentAI(api_key="YOUR_TENSORLAKE_API_KEY")

# Define page classifications
page_classifications = [
    PageClassConfig(
        name="signature_page",
        description="Pages containing signatures, signature lines, or signature blocks"
    ),
    PageClassConfig(
        name="terms_and_conditions", 
        description="If the has Terms and Conditions as a section header, classify as terms_and_conditions"
    )
]

parse_id = doc_ai.parse(
    file="https://pub-226479de18b2493f96b64c6674705dd8.r2.dev/Fake_Terms_Conditions.pdf",
    page_classifications=page_classifications
)
print(f"Parse job submitted with ID: {parse_id}")

# Get the result
result = doc_ai.wait_for_completion(parse_id=parse_id)

for page_classification in result.page_classes:
    print(f"Classification: {page_classification.page_class}")
    print(f"Page: {page_classification.page_numbers}")

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Classification Results

When you use page classification, the parse results include a page_classifications field that contains an array of classification results: 
{
  "parse_id": "parse_xxxxx",
  "status": "successful",
  "page_classes": {
    "terms_and_conditions": {
      "page_class": "terms_and_conditions",
      "page_numbers": [
        1
      ]
    },
    "signature_page": {
      "page_class": "signature_page",
      "page_numbers": [
        2
      ]
    }
  },
  // ... other parse results
}
Each classification result includes:
  • page_class: The classification name you provided. This will match the name field in your PageClassConfig.
  • page_numbers: An array of page numbers (1-indexed) that match this classification.

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Combining with Structured Extraction

You can combine page classification with structured data extraction to only extract data from specific page types. This allows speeding up structured extraction in long documents, and often improves accuracy of extraction. Check out this Colab Notebook to see an example of combining Page Classification with Structured Extraction.

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Best Practices

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Writing Effective Descriptions

The quality of your page classifications depends heavily on the descriptions you provide. Here are some tips: Be specific and descriptive 
# Good
PageClassConfig(
    name="financial_summary",
    description="Pages containing financial summaries, balance sheets, income statements, or tables with monetary values and financial metrics"
)

# Less effective  
PageClassConfig(
    name="financial_summary",
    description="Financial pages"
)
Include visual and content cues 
PageClassConfig(
    name="signature_page",
    description="Pages with signature lines, signature blocks, 'Sign here' text, or actual handwritten signatures. May include date fields next to signatures."
)
Mention common patterns 
PageClassConfig(
    name="form_page",
    description="Pages with form fields, checkboxes, fill-in-the-blank sections, or structured input areas for data entry"
)

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Common Use Cases

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Insurance Claims Processing

page_classifications = [
    PageClassConfig(
        name="claim_form",
        description="Insurance claim forms with policy numbers, incident details, and claimant information"
    ),
    PageClassConfig(
        name="supporting_documents", 
        description="Supporting documentation like police reports, medical records, or receipts"
    ),
    PageClassConfig(
        name="photos_evidence",
        description="Pages containing photographs, images, or visual evidence of damages"
    )
]

page_classifications = [
    PageClassConfig(
        name="contract_terms",
        description="Main contract pages with terms, conditions, and legal clauses"
    ),
    PageClassConfig(
        name="signature_pages",
        description="Pages requiring signatures from parties, with signature lines and date fields"
    ),
    PageClassConfig(
        name="exhibits_attachments",
        description="Exhibits, attachments, or addendums referenced in the main contract"
    )
]

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Financial Document Analysis

page_classifications = [
    PageClassConfig(
        name="executive_summary",
        description="Executive summary or overview pages with key financial highlights"
    ),
    PageClassConfig(
        name="financial_statements",
        description="Balance sheets, income statements, cash flow statements with numerical financial data"
    ),
    PageClassConfig(
        name="notes_disclosures",
        description="Footnotes, accounting policies, or disclosure pages explaining financial data"
    )
]
Page classification works with all supported document types including PDFs, Word documents, images, and more. The AI model analyzes both textual content and visual layout to make classification decisions.