Detect and extract table structures from scanned and image-based PDFs. AI-powered OCR handles merged cells, borderless tables, and multi-page tables — outputting clean Excel or CSV with the original layout preserved.
No templates. No grid-line detection rules. No per-document setup.
Upload a scanned PDF, image-based PDF, or document photo. The AI handles multi-page PDFs, low-resolution scans, skewed pages, and faded ink — no pre-processing required.
The AI identifies every table in the PDF, maps cell boundaries, detects merged cells and spanning headers, and extracts each value with its row-column position intact — even from borderless tables.
Export extracted tables to Excel, CSV, or Google Sheets with the original structure preserved. Multi-page tables are stitched into a single output. Use AI columns to define custom extraction rules in plain English.
Upload any scanned PDF containing tables — invoice, bank statement, financial report, or insurance form — and get structured spreadsheet-ready data back immediately.
AI reads table structures the way a person would — no gridline rules or templates.
AI identifies table regions within scanned PDF pages, distinguishing tables from surrounding text, headers, footers, and images. Detects tables even when page layout is complex with multiple content blocks, sidebars, and footnotes. No manual zone selection required.
Detects cells that span multiple rows or columns by analyzing visual alignment and content flow. Spanning headers are recognized and associated with their child columns. Multi-row cells are identified and mapped correctly to the Excel output, maintaining data relationships.
When a table spans multiple PDF pages, AI detects continuations by matching column structures, header patterns, and data types across page boundaries. Continued rows are stitched into a single logical table in the output — no manual page-by-page assembly needed.
Many scanned PDFs contain tables without visible gridlines. AI analyzes text alignment, column spacing, and content patterns to infer cell boundaries even when no lines are present. Works on financial statements, reports, and forms where whitespace is the only structural cue.
Upload hundreds of scanned PDFs at once. AI processes them in parallel, extracting all tables and outputting structured data to a single Excel workbook or individual files. Connect email, Google Drive, or cloud storage for automatic processing as PDFs arrive.
Export extracted tables to Excel, CSV, Google Sheets, or JSON with table structures intact. Each output format preserves row-column relationships. REST API returns structured JSON with confidence scores for automated pipeline integration.
“We scan thousands of bank statements per month. The tables used to require manual retyping because our old OCR tool just dumped flat text. Now the AI detects every table, including borderless ones, and the Excel output matches the original layout perfectly.”
“Our compliance team processes scanned regulatory filings with tables that span 4–5 pages. The multi-page table stitching is a game changer — we get a single clean Excel table instead of manually assembling data from individual pages.”
“Merged cells were our biggest headache. Every other tool either split them wrong or duplicated data. This is the first tool that actually handles spanning headers and multi-row cells correctly in the Excel output.”
“We cut manual table retyping by 95%. Scanned invoices, bank statements, and insurance forms with complex table layouts that used to sit in a backlog now process automatically with table structures intact.”
Operations teams using AI-powered PDF table OCR have reduced manual table extraction time by 85–95% across financial statements, invoices, regulatory filings, and scanned forms with complex table layouts.
Traditional OCR was built to convert images of text into machine-readable characters. It works well for that purpose — scanning a page of printed text and returning the words in order is a solved problem for modern OCR engines. But tables are fundamentally different from running text. A table is a two-dimensional data structure where the meaning of every value depends on its position relative to column headers, row labels, and other cells. Extracting the characters is the easy part. Preserving the structure is where most tools fail.
The core challenge is cell boundary detection. In a well-formatted digital PDF, table cells are defined by explicit grid lines that software can parse programmatically. But in a scanned PDF, those grid lines become pixels in an image — sometimes faded, sometimes partially cropped by the scanner, sometimes missing entirely in borderless tables. Rule-based approaches that look for horizontal and vertical lines fail whenever the scan quality degrades or the table uses whitespace instead of borders. The result is either a flat text dump that loses all structure, or a mangled grid where data ends up in the wrong cells.
Merged cells make the problem harder. Financial statements routinely use spanning headers where one cell covers multiple columns. Insurance forms use multi-row cells where a single description spans several data rows. Simple grid-detection algorithms assume every cell occupies exactly one row and one column. When they encounter a merged cell, they either split it incorrectly — duplicating content across cells that should be empty — or collapse adjacent cells together, losing data boundaries. Correct handling requires understanding which cells are logically merged by analyzing content flow and visual alignment, not just grid coordinates.
Multi-page tables add another layer of complexity. A bank statement's transaction table may span three pages. A regulatory filing's data table may continue across five. Each page break interrupts the table, often repeating headers or adding page numbers in the middle of the data. Naively processing each page independently produces fragmented tables that must be manually reassembled. AI-powered PDF table OCR detects table continuations across page boundaries, matches column structures, and stitches the fragments into a single coherent table.
Lido is a layout-agnostic AI extraction platform that handles PDF table OCR end to end. Upload scanned PDFs containing any table layout — borderless, merged cells, multi-page, nested — and get clean Excel output with table structures preserved. The AI reads visual layout the way a person would, detecting cell boundaries by alignment and context rather than grid lines. Teams using Lido for PDF table OCR report reducing manual table extraction by 85–95% across financial statements, invoices, insurance forms, and regulatory filings.
Audited security controls verified over a sustained period.
BAA available for healthcare and financial document processing.
Bank-grade encryption at rest. TLS 1.2+ in transit.
Documents never used to train or improve AI models.
PDFs automatically deleted within 24 hours of processing.
PDF table OCR is the process of using optical character recognition combined with AI to detect and extract table structures from scanned or image-based PDFs. Unlike basic OCR that returns flat text, PDF table OCR identifies rows, columns, headers, merged cells, and cell boundaries, then maps extracted data into structured Excel or CSV output with the original table layout preserved. Tools like Lido understand visual table layout and map each cell to the correct spreadsheet position without templates.
Modern AI-powered PDF table OCR achieves 95–99% character accuracy on clear printed documents and 92–98% table structure detection accuracy on standard tabular layouts. The critical metric is structure accuracy — whether cell boundaries, merged cells, and column relationships are detected correctly. Lido's AI reads visual layout to map each cell to the correct spreadsheet position, achieving higher effective accuracy than rule-based grid detection tools on real-world scanned PDFs.
Yes. Many scanned PDFs contain tables without visible gridlines, where whitespace and text alignment are the only structural cues. AI-powered PDF table OCR analyzes text alignment, column spacing, and content patterns to infer cell boundaries even when no lines are present. Lido detects borderless tables by reading visual layout cues the same way a person would.
AI detects merged cells by analyzing visual alignment and content flow across row and column boundaries. Spanning headers are recognized and associated with their child columns. Multi-row cells are identified by comparing cell boundaries across adjacent rows. This ensures merged cells in the original scanned PDF map correctly to the Excel output, maintaining data relationships.
Yes. AI detects when a table continues across page boundaries by matching column structures, header patterns, and data types between pages. Continued rows are stitched into a single logical table in the output. This is critical for financial statements, inventory reports, and any scanned PDF where tables routinely span multiple pages.
Lido is SOC 2 Type 2 certified and HIPAA compliant, with AES-256 encryption at rest and TLS 1.2+ in transit. Uploaded PDFs are automatically deleted within 24 hours. Documents are never used to train AI models. A signed Business Associate Agreement is available for healthcare and financial documents.
Lido offers 50 free pages with no credit card required. The Standard plan is $29/month for 100 pages. The Scale plan is $7,000/year for up to 42,000 pages and 10 users. Enterprise plans start at $30,000/year with custom ERP integrations, a dedicated account manager, and BAA signing for HIPAA compliance. Volume pricing is available for high-volume workflows.
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