Use Tensorlake to classify pages so that you can extract structured data only from relevant pages within your documents.
Page Classification enables you to automatically categorize pages within documents based on their content and purpose.
This powerful feature allows you to selectively extract structured data from only the relevant pages, improving efficiency and
accuracy for document processing workflows.
Page Classifications work by analyzing each page of a document and assigning it to one or more predefined categories that you
specify. This is particularly useful for:
Multi-section documents: Contracts, reports, or forms with different types of content on different pages
Selective data extraction: Only extracting structured data from specific page types (e.g., signature pages, form pages)
Document routing: Processing different page types with different workflows
Content organization: Understanding the structure and layout of complex documents
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
During parsing, Tensorlake analyzes each page and determines which of your defined classifications apply. If you have specified
page classifications, the parse results will include*:
A list of page classifications with the specific page numbers that match each category
You can combine page classification with structured data extraction to only extract data from specific page types.
When you specify page classifications in your structured extraction options, Tensorlake will only extract structured
data from pages that match those classifications.Check out this Colab Notebook to
see an example of combining Page Classification with Structured Extraction.
The quality of your page classifications depends heavily on the descriptions you provide. Here are some tips:Be specific and descriptive
Copy
Ask AI
# GoodPageClassConfig( 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
Copy
Ask AI
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
Copy
Ask AI
PageClassConfig( name="form_page", description="Pages with form fields, checkboxes, fill-in-the-blank sections, or structured input areas for data entry")
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" )]
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.