Table Detection
Automatically detect and extract tables from documents
TableFlow’s table detection technology can automatically identify and extract tabular data from various document formats, including PDFs, images, and spreadsheets. This capability is essential for processing documents with complex layouts, such as invoices, financial statements, and reports.
How Table Detection Works
TableFlow uses advanced AI algorithms to:
- Identify Tables - Detect table structures within documents, even when they don’t have clear borders
- Recognize Column Headers - Identify and map column headers to your template
- Extract Cell Data - Extract data from individual cells within the table
- Maintain Structure - Preserve the relationships between rows and columns
- Handle Multiple Tables - Identify and process multiple tables within a single document
Supported Table Types
TableFlow can detect and extract data from various table formats:
Well-Structured Tables
Tables with clear borders, consistent column spacing, and distinct headers.
Borderless Tables
Tables without visible borders, using whitespace or alignment to define structure.
Nested Tables
Tables within tables, common in complex financial or medical documents.
Tables Across Multiple Pages
Tables that continue from one page to another in multi-page documents.
Tables in Images
Tables within scanned documents or photographs.
Table Extraction Capabilities
Column Mapping
TableFlow automatically maps detected columns to the columns defined in your template. The AI considers:
- Column header text
- Data format
- Column position
- Context surrounding the table
Row Identification
The system identifies individual rows within tables, maintaining data relationships even in complex layouts.
Data Validation
After extraction, table data is validated against rules defined in your template, ensuring quality and consistency.
Best Practices for Table Detection
To get the best results from TableFlow’s table detection:
- Define Clear Table Structures - Create templates with well-defined table structures
- Use Descriptive Column Names - Clear column names help the AI correctly map detected columns
- Set Appropriate Data Types - Define expected data types for each column
- Apply Validations - Add validations to ensure the extracted data meets your requirements
- Test with Sample Documents - Process representative samples to refine your template
Example Use Cases
Invoice Line Items
Extract product details, quantities, prices, and totals from invoice line item tables.
Financial Statements
Extract transaction lists, account summaries, and balance details from financial documents.
Inventory Reports
Extract product listings, quantities, and locations from inventory documents.
Handling Table Detection Errors
If the AI encounters problems during table detection:
- Review extraction results for accuracy
- Adjust your template if necessary
- Apply manual corrections to improve future extractions
- Consider processing clearer copies of documents if quality is an issue
Next Steps
Learn about data types and how they help ensure the correct format for your extracted data.