Pull merchant names, dates, line items, totals, tax, and tips from receipt PDFs into organized spreadsheets for expense reports and bookkeeping
Receipts are the most chaotic document type in business finance. They arrive as crumpled thermal paper photos, email PDF attachments from online purchases, credit card terminal slips, and scanned bundles from traveling employees. Unlike invoices that follow relatively standardized layouts, receipts vary enormously: a Starbucks receipt looks nothing like a Home Depot receipt, which looks nothing like an Uber ride receipt, which looks nothing like a hotel folio. Yet all of them contain the same fundamental data that needs to end up in a spreadsheet: who was paid, when, for what, and how much.
The manual process of entering receipt data is uniquely tedious because of the volume and variety involved. A single employee on a five-day business trip might generate 30 to 50 receipts: meals, taxis, parking, office supplies, client entertainment, and incidentals. A sales team of 20 people produces 600 to 1,000 receipts per month. Each receipt requires identifying the merchant, reading the date, finding the total, separating tax from the subtotal, and categorizing the expense. This manual work typically happens at the end of each month when employees scramble to complete their expense reports, creating a bottleneck in the finance department that delays reimbursements and closes.
Lido extracts structured data from receipt PDFs using AI that understands receipt layouts from any merchant, POS system, or format. Upload a folder of receipt scans and get a clean expense spreadsheet with merchant names, dates, amounts, tax breakdowns, and categories extracted automatically. No templates, no per-merchant configuration. Start with 50 free pages.
Thermal paper degradation. The majority of physical receipts are printed on thermal paper, which degrades rapidly. Within weeks, thermal print fades, smudges, or becomes partially illegible. By the time receipts are scanned or photographed for expense reporting, the text quality has often deteriorated significantly. The AI must read faded characters, reconstruct partially missing text, and use contextual clues (like knowing that a number after a dollar sign is a price) to produce accurate extractions from degraded thermal prints. This is a fundamentally different challenge than extracting data from clean digital PDFs.
No standardized layout. While invoices generally follow a header-line items-totals structure, receipts have no universal format. Some receipts list items vertically with prices aligned to the right. Others use a compact format with descriptions and prices on the same line. Gas station receipts show gallons, price per gallon, and total in a different arrangement than retail receipts. Restaurant receipts have unique sections for food, beverages, subtotal, tax, tip, and total that don't appear on other receipt types. Hotel folios itemize charges by day with room rates, taxes, parking, and minibar as separate line groups. The AI must recognize each of these patterns and extract the relevant fields accordingly.
Handwritten annotations. Physical receipts frequently contain handwritten additions: tip amounts on restaurant receipts, notes about the business purpose, client names for entertainment expenses, or corrections to printed amounts. The AI must distinguish between printed text (the original receipt data) and handwritten text (annotations added afterward), extracting both but categorizing them correctly. A handwritten "Client dinner - Acme Corp" at the top of a restaurant receipt should be captured as a note field, not confused with the merchant name.
Employees traveling internationally collect receipts in multiple currencies. A European business trip generates receipts in euros, British pounds, Swiss francs, and Scandinavian kroner, sometimes with amounts shown in both the local currency and a converted amount. The AI identifies the currency from symbols, codes, or contextual clues (a receipt from a Paris restaurant is in euros even if the euro symbol is partially faded) and preserves the original currency in the extracted data. This enables finance teams to apply the correct exchange rates during expense reconciliation rather than guessing at currencies from ambiguous receipts.
Receipt extraction begins with document classification. The AI determines what type of receipt it is examining: retail purchase, restaurant meal, fuel purchase, hotel stay, transportation, or service receipt. This classification informs the extraction strategy, because different receipt types contain different fields. A restaurant receipt has tip and gratuity fields that a retail receipt does not. A hotel folio has check-in and check-out dates, room rates, and per-night breakdowns that are unique to lodging receipts. A gas station receipt has gallons, price per gallon, and fuel grade fields specific to fuel purchases.
After classification, the AI extracts the universal fields present on all receipts: merchant name, merchant address, date and time of transaction, payment method (cash, card type, last four digits), and total amount. It then extracts type-specific fields based on the classification. For retail receipts, this includes individual line items with quantities and prices. For restaurant receipts, it separates food subtotal, beverage subtotal, tax, tip, and grand total. For hotel folios, it captures daily room charges, resort fees, parking, and itemized incidental charges.
The output maps each receipt to a row in the spreadsheet, with consistent columns across receipt types. Universal fields like merchant, date, and total always appear in the same columns. Type-specific fields occupy additional columns that are populated when applicable and left empty when the field does not apply to that receipt type. This produces a single, unified expense dataset from a mixed collection of receipt types, ready for import into expense management platforms, accounting software, or corporate card reconciliation workflows.
Beyond raw data extraction, the AI assigns expense categories based on the merchant name and receipt content. A receipt from an airline is categorized as travel. A receipt from a restaurant is categorized as meals and entertainment. A receipt from a gas station is categorized as fuel or transportation. These categories follow standard expense report taxonomies and can be mapped to the specific chart of accounts codes used by your organization. Category assignment reduces the manual work of classifying each expense during report preparation, which is often the most time-consuming part of the expense reporting process after the initial data entry.
Monthly employee expense reports. Employees collect receipt photos and digital receipt PDFs throughout the month. At month-end, they upload the full collection for batch extraction. The AI produces a categorized spreadsheet that matches the company's expense report format, with each receipt identified by merchant, date, amount, and category. This eliminates the most painful part of expense reporting for employees and gives finance teams clean, consistent data for review and reimbursement processing. For companies processing receipts alongside vendor invoices, the same AI handles both document types.
Corporate card reconciliation. Finance teams reconciling corporate card statements need to match each card transaction to a supporting receipt. Extracting receipt data into a spreadsheet with dates, amounts, and merchant names enables automated matching against card statement transactions. Unmatched items are flagged immediately rather than discovered during the monthly close, reducing the reconciliation cycle from days to hours. The receipt extraction captures the last four digits of the card number when printed on the receipt, enabling direct matching against the card statement.
Tax-deductible expense documentation. Self-employed professionals and small business owners need to track deductible expenses throughout the year for tax preparation. Receipt extraction produces an organized spreadsheet of all business expenses with dates, amounts, merchants, and categories that maps directly to Schedule C expense categories for sole proprietors or to the appropriate expense lines for corporate tax returns. The structured data serves as audit-ready documentation that substantiates deductions with specific receipt details rather than generic category totals.
Per diem and travel policy compliance. Organizations with per diem policies or meal spending limits need to verify that employee receipts fall within allowed thresholds. Extracting receipt data enables automated comparison of meal totals against per diem rates by city and date. Receipts that exceed daily limits or fall outside covered categories are flagged automatically. This transforms travel policy compliance from a manual review process to a rules-based automated check that runs across all employee submissions simultaneously.
Upload scanned receipts, email receipt PDFs, or photographed receipts and get structured expense data in seconds
Yes. Most receipt PDFs originate from photos taken on a phone or scanned on a multifunction printer, resulting in image-based PDFs rather than native digital documents. The AI applies OCR combined with receipt-specific understanding to read merchant names, dates, item descriptions, prices, taxes, and totals from these image PDFs. It handles common receipt photo issues like shadows, creases, faded thermal print, and partial cutoffs at the top or bottom of the receipt.
Restaurant receipt extraction captures the subtotal, tax, tip amount, and grand total as separate fields. When the tip is handwritten on a printed receipt, the AI reads the handwritten amount and distinguishes it from the printed totals. When the tip line is blank or crossed out, it is recorded as zero. The AI also handles receipts where the tip is included as an automatic gratuity line item, separating it from voluntary tips for accurate expense categorization.
The AI processes thermal paper receipts (photographed or scanned), digital PDF receipts from online purchases and email confirmations, credit card terminal receipts, handwritten receipts, and point-of-sale system printouts. It handles receipts from retailers, restaurants, gas stations, hotels, airlines, ride-share services, and office supply stores. Each receipt type has different field conventions, and the AI adapts its extraction to the specific format encountered.
Yes. Upload all your receipt PDFs for a given period and the AI produces a consolidated spreadsheet with one row per receipt. Each row includes the merchant name, date, category, line items or description, subtotal, tax, tip, and total. This output is formatted for direct import into expense management systems or for attaching to expense reports. Batch processing handles hundreds of receipts in minutes rather than the hours required for manual entry.
50 free pages. All features included. No credit card required.