LIVE DEMO · NO EMAIL REQUIRED · POWERED BY REME'S OCR ENGINE
Drag-and-drop a receipt below — any photo, any language, any condition. REME's actual OCR engine extracts vendor, date, amount, line items, currency, and tax breakdown in real time. GST 2.0 native — CGST, SGST, and IGST extracted and separated for correct input tax credit recovery. Hindi receipts, Devanagari script, and mixed Hindi-English billing formats all handled. This is the same OCR powering REME's WhatsApp product. No signup, no email gate, no watermarked exports.
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Extracted data will appear here in real time.
Receipt images are processed via REME's production OCR API and not stored. Each request is ephemeral. For full audit trail and persistence, use REME's WhatsApp product.
REME runs this same OCR engine on every receipt employees submit via WhatsApp — automatically, in real time, with the extracted data flowing directly to your accounting system. Free 1-month trial.
Optical character recognition has been around for decades, but reliable receipt OCR is a 2020s capability. Receipts are uniquely hard for OCR engines: low-contrast thermal paper that fades, varying receipt formats across regions (a Hawker Centre receipt looks nothing like a Whole Foods receipt), multi-language merchants, and photographs taken at angles in moving taxis. The "98% accuracy" claim that's true on lab data drops to 70% in production for most engines.
REME's engine — the same one running the demo above — uses a five-stage pipeline that handles each of these failure modes specifically. Here's what's happening when you drop a receipt into the demo:
The raw image gets de-skewed, lighting-corrected, and contrast-enhanced. Faded thermal paper gets boosted. Photos taken at angles are perspective-corrected. Background clutter (table, hands, other objects in the photo) is masked out so only the receipt itself enters the OCR pipeline. This stage alone accounts for most of the accuracy gap between "lab demo" and "production reality."
A computer-vision model identifies every text element on the receipt — header text, line items, prices, dates, totals, footer text. At this stage, the model knows there are characters but doesn't yet understand what they mean. Output is a structured map of text positions and content.
A language model reads the recognized text in context and figures out structure. "Maxwell Food Centre" is the merchant. "$7.63" is the total (not the tax — context tells you that). "GST 9%" is the tax type and rate. "Stall 31" is a sub-merchant. This is where most generic OCR engines fail — they extract text but can't figure out what each piece means.
Each extracted field is cross-checked. Does the math add up (subtotal + tax = total)? Does the merchant exist (against a known merchant database)? Is the date plausible? Tax fields are enriched with jurisdiction metadata (this is a Singapore IRAS GST receipt, applying 9% standard rate). Confidence scores get assigned per field.
Structured JSON is returned to the demo (or, in REME's full product, to the customer's accounting system). Every correction made by users feeds back into the per-company learning model — so a company processing 1,000 receipts per month gets a model that's 10–15% more accurate after 90 days than it was on day one.
Most OCR vendors quote a single accuracy number ("99% accurate!") that's true on a clean test receipt scanned in good lighting. Production receipts don't look like test receipts. Here's the honest accuracy breakdown by receipt type — REME's, and roughly what you can expect from any modern OCR engine:
REME team: replace placeholder numbers with actual production accuracy data before publishing.
Industry averages from public OCR benchmarks (ICDAR, FUNSD datasets, vendor-disclosed numbers). REME's numbers from internal customer deployments — actual performance varies by receipt mix and deployment maturity.
Most "multi-language OCR" claims are vague. Here's the explicit list of what REME's engine handles natively — meaning the receipt structure is understood, not just the characters recognized. This is what the demo above is running against.
India is REME's deepest market. Hindi and Devanagari script are parsed structurally — not just transliterated. GST 2.0 (effective September 22, 2025) is natively supported with the current 0%, 5%, 18%, and 40% slabs, CGST+SGST+IGST component separation, and reverse charge handling. Mixed Hindi-English billing formats common across Indian retailers and B2B suppliers are handled correctly. Built for Indian finance teams first; tested globally second.
"Native structural parsing" means the engine knows where merchant name vs total vs line items are positioned in receipt formats from each region — not just transliterating characters.
Multi-component breakdowns supported (e.g., India's CGST + SGST + IGST shown separately for proper input tax credit recovery).
This demo shows OCR working on a single receipt. Useful for occasional one-off scans. But OCR is a small piece of the actual expense management workflow. Here's where standalone receipt scanners — including this one — hit their limits.
This demo handles single receipts. Real expense workflows generate hundreds of receipts per month per company. Submitting them one-by-one through a scanner is essentially the same friction as manual entry — just with OCR doing the typing. The win is at the workflow level: receipt → OCR → policy check → approval → posting, all automated.
This demo doesn't store your data — that's a privacy feature, but it means you can't come back tomorrow and see what you scanned. For real expense management, you need an audit trail: who submitted, when, with what receipt, approved by whom, posted to which GL account. That's what platforms provide.
OCR extracts the data but doesn't enforce your company policy. Is this expense within the meal cap? Is the merchant on the approved list? Does it need additional documentation? Without policy enforcement, the OCR output still requires manual review against your policy — which defeats the time-savings purpose.
OCR can read a receipt, but it can't tell you the receipt is a duplicate of one submitted last month, or that the merchant is fictitious, or that the amount has been altered. REME's full product runs 7 AI fraud detection agents on every claim before approval — something a standalone scanner can't do.
If your team processes more than 50 receipts per month, the standalone-scanner approach stops being efficient. REME's full WhatsApp-based product runs this OCR plus policy enforcement, fraud detection, approval routing, and accounting integration — automatically.
RECEIPT SCANNER FAQ
100% free, no email required, no signup. Your receipt image is processed via REME's production OCR API and is not stored — each request is ephemeral. The extracted data displays on your screen for the duration of your session and isn't saved to REME's database. Demo usage is rate-limited to prevent abuse (50 requests per hour per IP). For full persistent receipt storage, audit trail, and team workflows, use REME's full product.
For clean printed receipts (POS receipts in good lighting), REME hits ~99% accuracy on header fields (vendor, date, amount, tax). Performance varies by receipt difficulty: faded thermal paper (~90-95%), mobile photos in good lighting (~96-99%), low-light or angled photos (~85-92%), handwritten amounts (~60-75%), multi-language non-Latin scripts (~88-95%). These are honest production numbers — published more openly than most competitors who only quote "99% accuracy" on lab data. The full breakdown is in the accuracy benchmarks section above.
11 languages with native structural understanding (not just character recognition): English, Mandarin (Simplified + Traditional), Bahasa Indonesia, Bahasa Melayu, Hindi, Tamil, Thai, Vietnamese, Arabic, French, and German. New languages added quarterly based on customer geographic distribution. "Native structural understanding" means the engine knows the spatial layout of receipts in each region — a Hawker Centre stall receipt vs. a NYC bodega receipt vs. a Mumbai restaurant bill all have different formats, and REME parses each correctly.
8 native tax engines: IRAS (Singapore, GST 9%), GST India (with CGST + SGST + IGST multi-component breakdown), GST Australia (10%), VAT UK (20% standard, 5% reduced), VAT EU member states (country-specific), IRS US (sales tax by state where applicable), HMRC UK (supplementary), FTA UAE + GCC (5% VAT). The engine identifies the jurisdiction from the receipt — typically from merchant location or tax registration number — and applies the correct rules automatically.
Each extracted field gets a confidence score (0–100%). Fields below the confidence threshold (default 90%) are flagged for human review rather than silently flowing through with bad data. In the demo above, you'll see a confidence percentage in the top-right of the extraction results. In REME's full product, low-confidence fields trigger a follow-up message asking the submitter to confirm or correct — turning OCR mistakes into learning data for the per-company model.
Phone built-in scanners (iOS, Android) are designed for personal expense tracking — they extract amount and date well but typically miss line items, multi-jurisdiction tax breakdowns, and merchant categorization. They also don't integrate with company accounting systems or apply policy rules. REME's OCR is purpose-built for business expense workflows: 40+ fields extracted, multi-jurisdiction tax, line items, merchant categorization, integration with accounting systems. Different category of tool.
Yes. REME offers an OCR API for enterprise customers who want to embed the same extraction engine in their own applications. Pricing is per-extraction with volume tiers. For details, contact our sales team via the demo booking page. The free demo on this page uses a rate-limited public-tier endpoint; production API access is on-request.
To keep the demo accessible to as many people as possible without one user consuming all the OCR processing capacity. The current limit is 50 receipt extractions per hour per IP address. If you hit the limit, sign up for a REME free trial — full product usage isn't rate-limited at this level. Most users testing the demo will only process 5–10 receipts to evaluate accuracy, well below the rate limit.
Reasonably well. The pre-processing stage handles motion blur, low light, and perspective issues — but there are limits. A receipt photographed in near-darkness or with severe motion blur may have lower extraction confidence (typically 70-85% rather than 95%+). Best practice: take the photo in reasonable lighting with the receipt held steady, even if angled or partial. The photo doesn't need to be perfect.
Other free tools from REME plus relevant deep dives.
Free interactive template. Multi-currency, real-time totals, export to Excel/CSV/PDF. No signup required.
Open template → TOOL · COMING SOONMulti-component CGST/SGST/IGST breakdown with HSN code lookup. GST 2.0 rates (Sept 2025). Coming May 2026.
Notify me when ready → TOOL · COMING SOONAustralian GST 10% with business-expense input tax credit mode. Coming May 2026.
Notify me when ready → PRODUCTDeep dive into REME's 5-stage OCR pipeline — pre-processing, recognition, structure understanding, validation, and self-learning.
Read the deep dive → PRODUCTHow REME runs this same OCR on every receipt employees submit via WhatsApp. The full workflow from photo to posted GL entry.
See how it works → COMPARISONHonest comparisons of REME against Tabscanner, Veryfi, Dext, and other receipt OCR providers. Pricing, accuracy, integrations.
See comparisons →This demo handles one receipt at a time. Your team probably needs more.
WhatsApp submission. Same OCR engine you just tested. Multi-currency, multi-jurisdiction, real-time fraud detection. Continuous reconciliation to your accounting system. Backed by our adoption guarantee — if your team doesn't hit 80% in 30 days, we waive the next 60 days of paid usage.
The Adoption Guarantee
If your team doesn't hit 80%+ adoption within 30 days of rollout, we waive the next 60 days of paid usage. WhatsApp-based submission delivers 90%+ adoption in week one — we put our pricing where our promise is.
