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Extracting data from vehicle window stickers

A manufacturer fixes a window sticker, the Monroney label, to every new car before it reaches the dealer, and each brand designs its own layout. Auto lenders and insurers receive one with each new-vehicle deal they fund or cover, as a phone photo of the glass or a PDF reprint pulled by VIN.

An underwriter needs the base MSRP, each priced option, the destination charge, the total MSRP and the VIN. A loan origination system can cap the advance at a share of that total. An insurer's rating system uses it to value a newly bought car.

Page 1 of a sample Vehicle Window Sticker, with extracted values outlined
6,595Markdown characters
58parse blocks
74fields extracted
The problem

What makes vehicle window stickers hard

Each manufacturer prints its own layout

Federal law sets what the label must show, and each manufacturer decides where. Option lists and panel order change from brand to brand.

Four regulated panels share one sheet

One label carries the price list, the EPA fuel economy label with its rating sliders, the NHTSA star ratings and the parts content statement.

The label stays on the glass

The law keeps the sticker on the car until the buyer takes delivery, so copies often arrive as phone photos with glare and skew.

How it works

Parse, extract, verify

1

Parse

Parse turns the document into reading-order Markdown and a hierarchy of blocks (text, tables, figures), and grounds each block to its exact location on the page.

2

Extract

You define the fields in a JSON schema, and Extract returns their values in a consistent, structured format, each linked back to where it appears in the Markdown.

3

Verify

Each value links back to where it appears, down to the character range in the Markdown, and the page and bounding box in the source document, so every result can be verified.

Worked example

One vehicle window sticker, end to end

About this document: The Monroney label of a Kia Niro EX Touring on an auto show display car, one landscape image cropped from a phone photo. It carries five panels: standard features, the price column, the EPA fuel economy label, the NHTSA safety ratings and the parts content statement. Ceiling lights glare off the display glass. The EPA label prints its combined MPG in large numerals beside a fuel pump icon.

Processing objective: The objective is to capture what the car costs to buy and to run, for a lender's loan-to-value check and an insurer's valuation. The price column reconciles: the 31,490.00 base MSRP, 480.00 in priced options and the 1,325.00 freight charge make the 33,295.00 total.

Demonstration fields: Base MSRP, Inland freight and handling, Total MSRP, Combined MPG, Annual fuel cost

The document

1 page
  • Page 1 of the sample Vehicle Window StickerPage 1shown below

Where each value came from

Page 1 of the sample Vehicle Window Sticker, with every featured field outlined
Base MSRPpricing.base_msrp
31,490
The region of the sample Vehicle Window Sticker containing Base MSRP, with the extracted value outlined
extraction_metadata["pricing.base_msrp"]
{
  "value": 31490,
  "page": 1,
  "range": {
    "end": 1932,
    "start": 1921
  },
  "box": {
    "xmin": 0.50599,
    "ymin": 0.15368,
    "xmax": 0.56374,
    "ymax": 0.17597
  }
}
Inland freight and handlingpricing.inland_freight_and_handling
1,325
The region of the sample Vehicle Window Sticker containing Inland freight and handling, with the extracted value outlined
extraction_metadata["pricing.inland_freight_and_handling"]
{
  "value": 1325,
  "page": 1,
  "range": {
    "end": 3060,
    "start": 3051
  },
  "box": {
    "xmin": 0.50597,
    "ymin": 0.80883,
    "xmax": 0.56394,
    "ymax": 0.85666
  }
}
Total MSRPpricing.total_msrp
33,295
The region of the sample Vehicle Window Sticker containing Total MSRP, with the extracted value outlined
extraction_metadata["pricing.total_msrp"]
{
  "value": 33295,
  "page": 1,
  "range": {
    "end": 3141,
    "start": 3131
  },
  "box": {
    "xmin": 0.50597,
    "ymin": 0.85632,
    "xmax": 0.56395,
    "ymax": 0.89345
  }
}
Combined MPGfuel_economy.combined_mpg
49
The region of the sample Vehicle Window Sticker containing Combined MPG, with the extracted value outlined
extraction_metadata["fuel_economy.combined_mpg"]
{
  "value": 49,
  "page": 1,
  "range": {
    "end": 4162,
    "start": 4144
  },
  "box": {
    "xmin": 0.5694,
    "ymin": 0.231,
    "xmax": 0.66568,
    "ymax": 0.30424
  }
}
Annual fuel costfuel_economy.annual_fuel_cost
900
The region of the sample Vehicle Window Sticker containing Annual fuel cost, with the extracted value outlined
extraction_metadata["fuel_economy.annual_fuel_cost"]
{
  "value": 900,
  "page": 1,
  "range": {
    "end": 4413,
    "start": 4409
  },
  "box": {
    "xmin": 0.60023,
    "ymin": 0.41775,
    "xmax": 0.67788,
    "ymax": 0.46911
  }
}

How to try it yourself

Everything on this page is reproducible. The sample, its schema, and the Parse and Extract API outputs are published. You can repeat the same two calls from whichever surface you already build on.

  • Claude Skilldescribe the job; the agent writes it
  • CLIno code at all
  • MCPexpose ADE docs as an agent tool
  • REST APIany language
  • Pythonlandingai-ade
  • TypeScriptlandingai-ade
Why LandingAI?

Documents into data

Accurate

The best accuracy on the difficult documents other tools get wrong, such as messy scans, complicated tables, checkboxes and handwriting.

Auditable

Every answer cites its sources. Values come back with an audit trail (page, bounding box, character range) so a human can review them.

Agentic

Applies multi-step reasoning, and plans before it acts. Handles documents it has never seen before and adapts to variations.

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Composable APIs that drop into any tech stack and can be called from anywhere. Easy to build on and embed in your pipelines and products.

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Capabilities

Vehicle Window Sticker processing highlights

Base and total MSRP
Option rows with prices
Options marked Included
Destination charge
City, highway and combined MPG
Annual fuel cost
Greenhouse gas and smog ratings
NHTSA star ratings grid
Parts content and assembly point
VIN, trim and colors
Phone photos through glass
Bounding box for every value
FAQs

Questions, answered

Yes. Parse accepts phone photos as JPEG or PNG files and reads the label straight from the image. Bounding boxes come back as fractions of the image's width and height, so each value can be drawn back onto the photo a reviewer sees.

Define the options as an array with a description, a price and an included flag. Every row comes back in printed order. A row that reads Included returns the flag set and a null price, so the priced options can be summed against the total.

The EPA label's rated figures. That covers combined, city and highway MPG, gallons per 100 miles and annual fuel cost. It also covers the five-year fuel cost comparison, the two tailpipe ratings and CO2 grams per mile, each returned as a number.

No. A price printed with a dollar sign and thousands separators comes back as a plain number, ready for loan-to-value arithmetic. Grounding points at the printed string, so a reviewer sees the figure as the sticker shows it.

Each extracted value carries ranges into the parsed Markdown. The Ground API maps those ranges to the blocks they came from, with the page and bounding box of each. A total MSRP can then be highlighted on the label for an underwriter to check.

Start extracting vehicle window sticker data

Reliable, structured outputs with full traceability in minutes