Extract payee, amount, date, memo, or MICR data on demand—with customizable columns that match your downstream system’s import format.
Drag and drop files, connect a cloud drive, or set up email auto-forwarding. Any file format works—PDF, JPEG, PNG, TIFF, or digital documents.
The AI identifies fields by context and meaning, not fixed coordinates. Names, dates, amounts, and custom fields are extracted automatically.
Get structured output in Excel, Google Sheets, CSV, or JSON. Use the REST API for direct integration into your systems.
“We process EOB checks from 50 different insurance payers. The AI column that parses claim IDs from the memo line cut our payment matching time by 80 percent.”
“I needed vendor name, amount, and invoice number from each check—nothing else. The column customization let me set that up in minutes instead of cleaning data after export.”
“Payroll reconciliation was our monthly nightmare. Now check images go straight to a spreadsheet with employee name, net pay, and check number. No more manual transcription.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
A check extractor is a tool that reads bank checks—scanned, photographed, or received as digital images—and pulls out specific fields on demand rather than returning a fixed, one-size-fits-all dataset. The key distinction from generic check OCR is customizability: teams choose which fields appear in their output, how columns are named, and what transformations are applied, so the extracted data slots directly into their existing workflows without manual reformatting or column cleanup.
Different industries need different fields from the same check. An insurance company processing explanation-of-benefits checks needs the claim reference from the memo line, the payment amount, and the payer name. An accounts payable team logging vendor checks needs the vendor name, invoice number, amount, and check date. A payroll department digitizing paycheck records needs the employee name, net pay, pay period, and check number. A rigid extraction tool that returns every field in a fixed format forces each of these teams to clean, filter, and reformat the output before it is usable.
Customizable column mapping solves this by letting each team define their own output schema. In Lido, you add or remove columns, rename them to match your import format, and create AI columns that derive new values from the raw extraction—such as parsing a claim ID from a memo line, mapping payee names to vendor IDs, or flagging amounts that exceed a threshold. The result is a clean, purpose-built dataset that requires no post-processing.
For recurring workflows, email auto-forwarding makes the extraction hands-free. Forward check images to a dedicated inbox and they are processed automatically with your configured column mapping. Insurance payment teams, AP departments, and payroll groups each maintain their own extraction template, and incoming checks are routed to the right one based on the forwarding address. The output accumulates in a spreadsheet or flows through the API to downstream systems without anyone touching a scanner or typing a value.
Yes. Lido’s check extractor lets you define exactly which columns appear in your output. If you only need payee, amount, and check number, configure those three columns and ignore the rest. You can also add custom AI columns that derive new values from the extracted data, such as categorizing payments by payee name or flagging amounts above a threshold.
Insurance companies receive explanation of benefits (EOB) checks from payers with reference numbers, claim IDs, and payment amounts that need to match against open claims. A check extractor pulls the payee, amount, date, check number, and memo line where claim references typically appear. Lido’s AI columns can parse the memo field to isolate claim IDs, making it possible to auto-match payments to claims without manual lookup.
Customizable column mapping means you control the column names, order, and data transformations in your output file. Instead of receiving a fixed set of columns labeled by the tool, you rename them to match your system’s import format, reorder them as needed, and add computed columns. For example, you might rename “Numeric Amount” to “Payment” and add a column that maps payee names to vendor IDs from a lookup table.
Yes. Payroll checks typically include the employee name, net pay amount, pay period dates, check number, and sometimes earnings breakdowns on an attached stub. Lido’s check extractor captures the check face fields and can be configured with AI columns to pull additional data from attached stubs or remittance advice. The output feeds directly into payroll reconciliation spreadsheets or HR systems.
Upload or forward vendor check images to Lido and the extractor pulls the vendor name (payee), payment amount, check date, check number, and memo or invoice reference. These fields map directly to the columns in a vendor payment log. Set up email auto-forwarding so incoming vendor checks are extracted and logged automatically without manual scanning or data entry.
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Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine