Pull Driver Name, ETA, and Live Delivery Status from Any Food Delivery Tracking Page Screenshot
Manually typing driver name, ETA, and delivery status from a DoorDash tracking screen into a log takes 2 minutes per order — this pulls all visible tracking data in 5-10 seconds per screenshot.
5-10s per screenshot · Up to 99% accuracy on printed text
What You Can Pull from a Delivery Tracking Page
A food delivery tracking page shows driver details, a moving map, and a countdown ETA — each app lays it out differently. With Custom Column Extraction, you type the field names once and the AI reads them by meaning across DoorDash, Uber Eats, Deliveroo, Wolt, and Meituan. Here are the fields you can target.
Identified by app branding and UI chrome — DoorDash (red/white), Uber Eats (black header), Wolt (blue accent), Deliveroo (teal).
Often in small text inside a driver card, collapsible panel, or bottom sheet — style varies per app.
"Silver Toyota Prius," "Red Honda Activa," or e-bike — sometimes paired with a license plate number.
Countdown timer or time range — "Arriving in 5-10 min," "Expected by 7:15 PM" — format differs per app.
"Your Dasher is on the way," "Courier heading to you," "Preparing your order" — same event, different phrasing across apps.
Your address as shown on the tracking screen — cross-check against the app's order destination.
This Isn't a Static Receipt — It's a Live Dashboard With a Moving Map, a Countdown, and App-Specific Chrome
A tracking page is fundamentally different from every other document type — it has no fixed layout, contains elements that change by the second, and wraps critical data points in app-specific UI that traditional OCR can't parse. Here is what makes it uniquely hard and how semantic extraction handles it.
DoorDash uses a map-first layout with a bottom-sheet driver card. Uber Eats places a status bar at the top. Wolt uses a centered info card. Deliveroo shows a circular driver photo with a timeline. Meituan combines a map with a status feed. Six apps, six fundamentally different UI architectures — none of which a template-based tool can read across.
The ETA is a countdown that updates every few seconds. The driver marker moves on a live map. Traditional OCR sees a static image with no concept of what these moving elements mean. As Reddit users report on r/UberEATS, the "arrival by" time changes frequently — "I took screenshots, because the driver was stationary for 15 or 20 minutes, and it should have taken 15 minutes" — screenshots are the only record of what appeared at that moment.
Driver names and vehicle info are rendered in small type — "Dasher: Priya in a silver Toyota Prius" in a collapsed card or bottom-sheet. Promotional banners ("Try DashPass!") compete for screen space alongside driver details. OCR reads everything as one flat text block, mixing delivery data with marketing noise.
With Custom Column Extraction, you define columns once — and the AI locates each value by meaning, not screen position. A name next to a driver icon or vehicle text is recognized as "Driver Name" whether it sits in DoorDash's bottom sheet, Uber Eats' top bar, or Wolt's centered card. The layout doesn't matter.
The AI recognizes delivery-phase semantics. "Your Dasher is on the way" (DoorDash), "Courier heading to you" (Deliveroo), "On the way" (Wolt), and "Your order is arriving soon" (Uber Eats) are all mapped as the en-route stage. The Current Status column preserves each app's original text, while a Platform column keeps the source identifiable.
The AI scans the entire tracking page and distinguishes delivery data from interface elements. A live map with a moving marker is recognized as a map widget — not confused with an address. Promotional banners and upsell CTAs are identified as non-tracking elements. Only the driver, ETA, status, and address populate the spreadsheet.
From a Mixed-App Batch of Tracking Screenshots to One Clean Spreadsheet
If you log deliveries from multiple apps — DoorDash for dinner orders, Wolt for groceries, Uber Eats for late-night — here is what happens when you drop all the screenshots in at once.
Upload tracking page screenshots from any delivery app
Drop in tracking page screenshots from however many apps you use — DoorDash, Uber Eats, Deliveroo, Wolt, Meituan, or any regional delivery platform. Full-resolution screenshots that capture the driver card, ETA counter, and delivery address in one frame produce the best results. Screenshots forwarded through messaging apps may lose clarity on small text like vehicle license plates.
Name the fields you want — the AI handles the rest
Define your columns: Platform, Driver Name, Vehicle, ETA, Current Status, Delivery Address. Add Platform (options: DoorDash, Uber Eats, Deliveroo, Wolt, Meituan, Other) as an Inferred Column — the AI identifies which app each screenshot belongs to by visual branding, so you don't have to sort or label anything before uploading.
Export to Excel — one row per delivery, all apps in one table
Each tracking page screenshot becomes a row in your spreadsheet. "Priya" in Driver Name. "Silver Toyota Prius" in Vehicle. "Arriving in 3 min" in ETA. Batch 15 mixed-app screenshots and the output is one clean table — no per-app setup, no sorting by platform, no manual copy-paste from each app.
Sample Output
| Platform | Driver Name | Vehicle | ETA | Current Status | Delivery Address |
|---|---|---|---|---|---|
| DoorDash | Priya | Silver Toyota Prius | 8:15 PM | Your Dasher is on the way | 742 Elm St, Apt 4B |
| Uber Eats | Marcus | Black Honda Civic · AB12 CDE | 8:22 PM | Arriving soon | 12 Oak Lane |
| Wolt | Jonas | E-bike | 5-10 min | On the way | Keskuskatu 3A, Helsinki |
Where Delivery Tracking Page Extraction Is Reliable — and Its Limits
A tracking page is not a receipt. It captures a single moment in a live delivery — here is what the tool handles well and where expectation matters.
- ✓
Full-screen, high-resolution tracking page screenshots that capture the driver card, ETA counter, and delivery address in one frame. Native-resolution captures from the app work best.
- ✓
Mixed-app batch processing — DoorDash, Uber Eats, Wolt, Deliveroo, and Meituan screenshots together under the same column definitions, no per-app configuration.
- ✓
Per-delivery proof logging — capturing a screenshot for each active delivery and extracting driver, ETA, and status into a consistent log for cross-app records.
- ⚠
This does not track deliveries in real time. It reads what is visible in the screenshot at the moment of capture — it does not connect to any delivery app's API, does not auto-refresh when a driver moves, and does not send push notifications when the status changes.
- ⚠
Screenshots from chat apps with image compression (WhatsApp, Messenger, WeChat) may reduce clarity on small UI text like vehicle license plates or driver names in 10pt type on the delivery card. Native in-app screenshots produce better results.
- ⚠
Non-English tracking pages (Meituan in Chinese, Baemin in Korean, Rappi in Spanish) may require language-specific column definitions for accurate field mapping.
Frequently Asked Questions
Can I mix DoorDash, Uber Eats, Deliveroo, and Wolt tracking page screenshots in the same batch?
Yes. Define one set of columns — Platform, Driver Name, Vehicle, ETA, Current Status, Delivery Address — and the AI processes every screenshot using the same definitions. DoorDash's bottom-sheet driver card, Uber Eats' status bar, and Wolt's centered card are all read semantically. Driver names and ETAs land in the correct columns regardless of where each app places them. No pre-sorting required.
Does the Current Status column recognize that different apps phrase the same delivery stage differently?
Yes. The AI reads the semantic meaning of each status phrase, not the exact wording. "Your Dasher is on the way" (DoorDash), "Courier heading to you" (Deliveroo), "On the way" (Wolt), and "Your order is arriving soon" (Uber Eats) are all recognized as the same en-route delivery stage. The extracted value preserves each app's original phrasing in the Current Status column, and the separate Platform column lets you distinguish which app's terminology you're looking at when you review the spreadsheet.
Can the tool tell which delivery app the screenshot came from without me labeling it manually?
Yes — this is a prime use case for Inferred Columns. Add a column named Platform (options: DoorDash, Uber Eats, Deliveroo, Wolt, Meituan, Other) and the AI identifies each platform by its visual branding: DoorDash's red-and-white color scheme, Uber Eats' black-header layout, Wolt's blue accent cards, Deliveroo's teal branding, and Meituan's yellow-and-orange UI. This means you can drop in a mixed batch of screenshots from different apps, and the Platform column fills itself — no pre-sorting required.
What if the tracking page has promotional content overlapping the driver info?
The AI scans the full visual scene and distinguishes delivery tracking data from interface chrome and promotional content. "Try DashPass!" banners, "Get 50% off your next order" CTAs, and "Sign up for alerts" widgets are identified as non-tracking elements and excluded from extraction. However, if a full-screen interstitial ad or modal overlay temporarily blocks the driver card or ETA at the time of capture, that data will not be readable in the screenshot. For the most complete output, capture the tracking page when no promotional modals are open.
Does this work for delivery tracking pages in languages other than English?
The AI reads text in over 60 languages, including Chinese (Meituan, Ele.me), Japanese (Uber Eats Japan), Korean (Baemin, Coupang Eats), and Spanish (Rappi, PedidosYa). Define column names in the language the AI is reading from, or use English column names and let the AI cross-map semantically. If a delivery platform embeds tracking inside a multi-tab panel (common with super-apps like Meituan), capturing a full-screen screenshot that isolates the tracking view produces more reliable extraction than capturing the entire app screen with multiple tabs visible.