AI detects and extracts bordered, borderless, merged-cell, and multi-page tables from any PDF. No templates. No manual selection. Just upload and get structured spreadsheet data.
No templates. No table region selection. No manual cleanup.
Upload financial reports, invoices, bank statements, government filings, or any PDF with tables. Drag and drop one file or hundreds. The AI handles any table structure, any layout, any scan quality.
The AI scans each page for table structures — bordered, borderless, merged cells, nested headers, multi-page continuations. It reconstructs each table with clean rows and columns, mapping data to the correct cells automatically.
Get your extracted tables in Excel, Google Sheets, CSV, or JSON. Every cell lands in the right position. Use AI columns to add custom extraction rules in plain English for non-table data.
Drop any PDF with tables below — financial reports, invoices, statements — and get structured spreadsheet data back immediately.
AI handles the table structures that break traditional extraction tools.
Extracts data from bordered tables, borderless tables, tables with alternating shading, and tables embedded within paragraphs. The AI reads spatial alignment and value patterns to reconstruct table boundaries regardless of visual styling.
Correctly interprets cells that span multiple columns or rows — common in financial summaries, insurance documents, and government reports. The AI maps merged cells to the correct position in the output spreadsheet without duplicating or losing data.
Detects when a table continues across page breaks and merges all continuation rows into a single output table. Handles headers that repeat on each page, headers only on the first page, and mid-row page splits without losing alignment.
Identifies tables that have no visible borders or gridlines by analyzing column alignment, row spacing, and data type patterns. Financial reports, academic papers, and regulatory filings frequently use borderless tables that rule-based tools miss entirely.
Upload hundreds of PDFs at once and extract all tables into a single spreadsheet. Connect an email inbox or cloud drive folder for automatic processing as new PDFs arrive. Batch mode handles mixed document types and table structures in the same upload.
Export extracted tables to Excel (.xlsx), Google Sheets, CSV, JSON, or XML. Each table preserves its original row and column structure. REST API returns structured JSON with cell-level confidence scores and table boundary metadata for developer integration.
“We process annual reports from 200+ companies and every one has different table layouts. Manually copying tables into Excel took our analysts hours. Now the AI extracts every table from a 50-page PDF in seconds, including the borderless ones that used to require manual transcription.”
“Our biggest pain point was multi-page tables in vendor invoices. The table would start on page 2 and continue through page 5, and no tool could stitch them together. This handles multi-page tables perfectly — one continuous table in the output with all rows intact.”
“We tried Tabula and Camelot first, but they failed on our government filings because of merged cells and nested headers. Switching to AI-powered extraction solved everything. The accuracy on complex table structures is consistently above 97%.”
“Our research team extracts tables from 500+ financial reports per quarter. We used to have analysts manually selecting and copying tables from PDFs into Excel — about 20 hours per person per week. Now it runs automatically and we just validate the flagged cells.”
Teams extracting tables from high-volume PDFs have eliminated manual data entry after switching to AI-powered table detection that handles any structure without templates.
PDF is a page-description format designed for printing, not data interchange. When a PDF contains a table, the file stores individual characters positioned at specific coordinates on the page. There is no semantic concept of "table," "row," "cell," or "column" in the PDF specification. A table that looks perfectly structured to a human reader is stored as hundreds of disconnected text fragments and optional line-drawing commands. Extracting structured data from this representation is fundamentally a reconstruction problem.
Rule-based extraction tools look for horizontal and vertical lines that form cell borders, then group the text fragments inside each cell. This works on tables with complete, visible gridlines but fails on the many table styles that omit some or all borders. Borderless tables — where column alignment and row spacing imply structure — are invisible to line-detection algorithms. Merged cells that span multiple columns or rows create gaps in the expected grid that cause rule-based tools to misalign subsequent cells. Multi-page tables introduce a second layer of difficulty: the tool must recognize that a table continues on the next page, match column structure across the page break, and merge the continuation rows without duplicating headers.
Nested headers add further complexity. Financial reports frequently use two or three levels of column headers where a parent header like "Q3 2025" spans three child columns for "Revenue," "Expenses," and "Net Income." Rule-based tools treat each header as an independent cell and lose the parent-child relationship, producing flat output that requires manual restructuring. Academic papers, government filings, and regulatory reports use similar nested structures that defeat tools relying on simple grid detection.
AI-powered table extraction takes a fundamentally different approach. Rather than looking for drawn lines, Lido analyzes the spatial relationships between all text elements on the page. It identifies column alignment patterns, consistent vertical spacing that indicates row boundaries, header text styles, and data type patterns (numbers, dates, currency values) to reconstruct the complete table structure. This works regardless of whether the table has visible borders, uses merged cells, spans multiple pages, or employs nested headers. The AI interprets the table the way a person would — by understanding the visual layout and the meaning of the data, not by counting pixel-level line segments.
The practical result is that teams working with complex PDF tables — financial analysts processing annual reports, compliance teams extracting data from regulatory filings, procurement managers pulling line items from multi-page invoices — can upload their PDFs and get clean, structured spreadsheet data without manual table selection, border detection tuning, or per-layout template configuration.
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AI-powered PDF table extraction handles bordered tables with visible gridlines, borderless tables defined by text alignment, merged cells that span multiple columns or rows, multi-page tables that continue across pages, nested headers with parent-child column groups, and irregular layouts with mixed widths and embedded sub-tables. The AI reconstructs table structure from spatial relationships between text elements rather than relying on visible cell borders.
AI-powered table detection analyzes the spatial layout of text elements on each PDF page. It identifies column alignment patterns, consistent row spacing, header styles, and value types to determine where tables begin and end. Unlike rule-based tools that look for drawn borders, AI interprets the visual structure the way a person would — recognizing that aligned numbers form a column and that bold text above them is a header, even without any gridlines present.
Yes. The AI combines OCR with table structure detection to extract tables from scanned documents, photographed pages, and image-based PDFs. It reads the text from the scan, then analyzes spatial relationships to reconstruct the table layout. This works on variable-quality scans, skewed pages, and documents with background noise. Accuracy on scanned PDF tables typically ranges from 90–98% depending on scan quality.
The AI detects when a table continues from one page to the next by matching column structure, header patterns, and data types across page boundaries. It merges continuation rows with the original table and preserves column alignment throughout. This works even when the header row is only printed on the first page and subsequent pages start directly with data rows.
No. Traditional table extraction tools require you to define extraction zones or table boundaries for each PDF layout. Lido uses layout-agnostic AI that detects table structure automatically from any PDF. It works on financial reports, invoices, government filings, research papers, and any other document type without templates, training data, or per-document configuration.
Yes. Lido is SOC 2 Type 2 certified and HIPAA compliant, with AES-256 encryption at rest and TLS 1.2+ in transit. All uploaded PDFs are automatically deleted within 24 hours of processing. Your documents are never used to train AI models. A signed Business Associate Agreement is available for organizations processing sensitive documents.
Extracted tables can be exported to Excel (.xlsx), Google Sheets, CSV, JSON, and XML. Each table is output with clean rows and columns preserving the original structure. For developers, a REST API returns structured JSON with cell-level confidence scores and table boundary metadata.
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