What file types can I upload?

PDF, PNG, JPG, and WEBP — including scanned titles and phone photos as well as digital PDFs.

Does it work on a photo of a paper title?

Yes. Docyield runs OCR before extraction, so a clear photo works alongside a scan. A flat, sharp, well-lit image gives the best read, since title security backgrounds can interfere with low-resolution photos.

Does it tell me if there is a lien on the vehicle?

It returns the lienholder named on the document. If the title records no lien, the lienholder field comes back empty rather than inventing one. This is data extraction, not legal advice.

Can it read titles from different states?

Yes. The parser reads each certificate by meaning rather than a fixed template, so it handles different state designs and returns the issuing state as its own field.

How accurate is the odometer reading?

Accuracy is high on clear documents, and the reading is returned with its unit where given. A worn or stamped odometer box can be misread, so check it against the source for any transaction that depends on it.

Is my title data kept private?

Files are used only to produce your result and are not used to train models. For high-volume or regulated workloads, the API and paid plans offer additional controls.

Can I process many titles at once?

The free tool handles one title at a time. For batch uploads, API access, and webhooks — such as an auction intake — Docyield offers paid plans built for volume.

What is the difference between a title and a registration?

A title records ownership and any lien on the vehicle, while a registration records the right to operate it on public roads for a period. Docyield offers a separate parser for each.

Car Title Parser

Car title OCR — extract title number, VIN, vehicle, owner, and lienholder details into JSON, CSV, or Excel.

Drag & drop your document here

Supports PDF, JPG, PNG, WEBP

Docyield's car title parser reads a certificate of title — scanned, photographed, or supplied as a PDF — and returns its key facts as structured data. A title is the document that establishes who owns a vehicle, so it carries the title number, the VIN, the vehicle's make, model, and year, the owner's name and address, any lienholder, the odometer reading recorded at issue, the issue date, and the state that issued it. The parser captures each of those into its own field and exports them as JSON, CSV, or Excel.

Titles are issued by state motor-vehicle authorities, and the designs vary widely — different colours, different security backgrounds, different field positions, and different wording for the same idea. A title from one jurisdiction may label the secured party a "lienholder" while another writes "first lienholder" or "secured party of record." Rather than depending on a fixed template, Docyield reads the certificate by meaning, so it locates the owner, the lienholder, and the odometer wherever a given state prints them.

Inputs

PDF, JPG, PNG, WEBP

Outputs

JSON · CSV · Excel · XML

Price

Free · no signup

What a certificate of title records

A car title is the legal record of ownership for a vehicle. At its core it links a VIN to an owner, but it carries more than that: the title number that identifies the certificate, the vehicle's make, model, and year, the owner's name and address, the date the title was issued, and the issuing state. Many titles also record an odometer reading captured at the point of issue or transfer, and a lienholder if money is still owed against the vehicle.

The lienholder line is the part that makes a title more than a registration. It shows whether a lender holds a security interest in the vehicle, which is exactly the fact a buyer, a dealer, or a lender wants confirmed before a sale closes. A clean parse pulls that line out plainly so there is no ambiguity about whether a lien is recorded. The make, model, and year sit alongside it as the vehicle description, and the year in particular often feeds a valuation, so it is captured as written rather than inferred from the VIN.

Why structured extraction beats reading it manually

Titles are dense and visually busy, with security patterns, fine print, and fields packed close together. Reading one by eye and copying the details into a system is slow, and the odometer and lienholder lines are easy to overlook on a crowded certificate. Plain OCR removes some of the typing but hands back unsorted text in which the owner address, the lienholder, and the issuing state are tangled together.

Pulling each value into a named field solves that. Because the VIN, the owner, the lienholder, and the odometer always land in the same places in the output, a dealer or title service can process a stack of titles and rely on the structure rather than re-reading each certificate to confirm whether a lien is present.

Who uses a car title parser

Used-car dealerships logging titles for vehicles taken in trade or bought at auction.
Lenders and finance offices verifying ownership and any existing lien before issuing a loan.
Title and registration services processing transfers between owners and across states.
Insurers confirming the titled owner and VIN when handling a total-loss or salvage claim.
Fleet and leasing operators tracking which vehicles they hold clear title to.
Developers adding title capture to an automotive or lending app via the API.

Lienholder, odometer, and the fields that carry weight

Two fields on a title carry outsized importance, and the parser treats them with care. The lienholder field is returned only when a secured party is named; if the title shows no lien, it comes back empty rather than inventing one, because a falsely recorded lien is as misleading as a missed one. The odometer field is captured with its unit where the document gives it, since a reading in miles versus kilometres is a meaningful difference.

The issuing state — the title state — is kept separate from the owner's address state, because a title issued in one jurisdiction can list an owner who has since moved. Keeping these distinct avoids the common manual error of assuming the two always match. The title number deserves the same separation: it identifies the certificate, not the vehicle, and conflating it with the VIN is a frequent slip when a title is read in a hurry, so the parser returns each in its own field.

Accuracy and where review pays off

No reader is right every time, and a title tool that promised perfection would be hiding its weak spots. Accuracy is highest on flat, sharp scans. The security backgrounds and watermarks that protect titles can interfere with low-resolution photos, and a worn or stamped odometer box is a classic source of misreads, so a clearer image always helps.

Because a title underpins ownership, any value that drives a sale, a loan, or a transfer is worth checking against the source. The parser keeps the original document beside the extracted fields, returns absent values empty rather than guessed, and surfaces the data so a quick human review of the lienholder and odometer is fast on the documents that warrant it. This describes data extraction only and is not legal advice.

Output formats, API, and batch

Every parse exports as JSON, CSV, Excel, or XML from the same result. CSV and Excel suit a dealer or title-service worklist; JSON suits a lending or DMS integration; XML fits an older back-office system. The free tool processes one title at a time.

When you handle titles in volume — an auction intake or a continuous transfer pipeline — the Docyield API and batch dashboard run the same extraction at scale, return results by webhook, and let you apply your own rules, such as flagging any title where a lienholder is present. The field names are identical between the free tool and the API.

What the car title parser extracts

Each title is returned against a fixed schema. Fields the document does not show — a lienholder on a clear title, for example — come back empty rather than guessed.

Title number: The number identifying the title certificate.
VIN: The Vehicle Identification Number.
Make: Vehicle make or manufacturer.
Model: Vehicle model.
Year: Model year.
Owner name: The titled owner's name.
Owner address: The owner's address.
Lienholder: The secured party, if any lien is recorded on the title.
Issue date: The date the title was issued.
Odometer: The recorded odometer reading, with its unit where given.
Title state: The state or jurisdiction that issued the title.

How to convert a car title to JSON, CSV, or Excel

1Upload your title — drop a flat scan, photo, or PDF of the certificate onto the box above, or choose a file.
2Wait a few seconds while Docyield reads the document and separates the VIN, owner, lienholder, and odometer.
3Review the structured result, checking the lienholder and odometer against the original.
4Pick your output tab — JSON, CSV, Excel, or XML.
5Copy the result or download the file, ready for your dealership, lending, or title-service system.