Vehicle title collateral record capture
For Vehicle finance collateral administrators
Prepare owner, VIN, jurisdiction and printed lienholder from a title image with fields you can edit. No title verification.
25 free credits/month · No card needed
See the workflow and review checksInspect a document example.
Synthetic documents · Expected output- owner name
- Example Owner - synthetic
- vehicle identification number
- SYNTHETIC-VIN-000042
- title reference
- DEMO-TITLE-0042
- jurisdiction
- CA - stated in the synthetic source
- printed lienholder
- Example printed lienholder - synthetic
- issue date
- 2026-09-30
{
"owner_name": "Example Owner - synthetic",
"vehicle_identification_number": "SYNTHETIC-VIN-000042",
"title_reference": "DEMO-TITLE-0042",
"jurisdiction": "CA - stated in the synthetic source",
"printed_lienholder": "Example printed lienholder - synthetic",
"issue_date": "2026-09-30"
}A complete synthetic document with the visible values shown below. Real responses also include model-reported confidence, validation and result status. Test your own file with this schema.
When this work lands on your desk
A title image arrives for vehicle finance collateral administrators. The next step needs owner, VIN, jurisdiction and printed lienholder, recorded under the correct document reference before the information enters your loan intake worksheet.
The title contains a prior lienholder and a separate release notation. Preserve the printed text and route the record for title review. This schema keeps the source labels and values explicit so the reviewer can resolve the issue before downstream use.
Start with the documents you already collect
- Title image PDFs
- Scanned or photographed title image pages
PDF, JPEG, PNG, TIFF, or WEBP. Send one file per extraction call; create separate schemas when the document types need different fields.
Define the fields
Open the starter schema. Edit field descriptions, mark the values your process requires, and save it in your account.
Extract at intake
Send the document and saved schema slug to
POST /api/v1/extract, or collect the file through a branded upload site.Hand off reviewed data
After checking owner name, vehicle identification number, title reference, your application maps the reviewed record into your loan intake worksheet. Keep the original document reference with the result. No title verification. Dokyumi processes one file with a saved schema; your application owns record matching, business rules and updates to other systems.
A schema shaped around the next step.
Start with these fields, then adjust them to match your documents and destination system. The example above shows the data object you are asking Dokyumi to extract.
Download editable starter schemaChoose required fields for your process. The starter schema preserves any defined nested fields; refine line-item structures and test them on representative files.
| Field | Type | What to capture |
|---|---|---|
owner_name | string | Owner name exactly as printed in this title image. Preserve identifiers and qualifiers; do not look up or infer missing information. |
vehicle_identification_number | string | The identifier printed for this vehicle. Preserve every character without asserting a title or ownership check. |
title_reference | string | Title reference exactly as printed in this title image. Preserve identifiers and qualifiers; do not look up or infer missing information. |
jurisdiction | string | Jurisdiction exactly as printed in this title image. Preserve identifiers and qualifiers; do not look up or infer missing information. |
printed_lienholder | string | Printed lienholder exactly as printed in this title image. Preserve identifiers and qualifiers; do not look up or infer missing information. |
issue_date | date | Issue date explicitly printed in this title image. Keep it distinct from other dates; leave it empty if no calendar date is stated. |
Give exceptions a clear path to review.
Dokyumi returns a model-reported confidence map, validation details, and a completed or review status for produced results. Check required fields and your business rules before passing data onward.
A completed extraction describes the extraction result. Your application and reviewers own matching, approvals, routing, and updates to other systems.
Read the response contractChecks for this process
- The title contains a prior lienholder and a separate release notation. Preserve the printed text and route the record for title review.
- Check issue date against their own printed labels; leave an unstated date empty instead of deriving it.
- Resolve missing owner name, vehicle identification number, title reference against the source before using the record.
Prove the fit on a representative batch.
Test title image examples from the layouts your team actually receives. Include the exception described here: The title contains a prior lienholder and a separate release notation. Preserve the printed text and route the record for title review. Compare the extracted owner name, vehicle identification number, title reference and any line rows with a reviewer's checked reference before enabling downstream actions.
Compare extracted values with a reviewer’s reference set. Record corrections, unresolved fields, review time, and credits used before connecting downstream actions.
The free plan’s credit limits apply. Assisted pilots are scoped with Dokyumi before work begins.
Expand when the next process is ready
Add recurring extraction capacity when title image intake becomes routine. Reuse this field contract for layouts that express the same information; create a separate schema when the next document has different fields or review requirements.
For a tailored implementation
Discuss document volume, page lengths and schema setup for vehicle finance collateral administrators. Bring difficult title image samples and the review requirements your implementation must preserve.
Talk through document mix and volumeStart small. Add capacity as intake grows.
All self-service plans include the REST API, upload-site webhooks, custom confidence thresholds, and schema inference. Choose by volume, schema count, and upload sites.
Each document uses one credit per 5 pages, rounded up. An 8-page file uses 2 credits. Self-service files can be up to 50 pages; larger files, custom-built schemas, and additional volume are quoted through Enterprise. Full pricing details
Questions about vehicle title collateral record capture
Which fields can I capture from a title image?
The starter schema contains owner name, vehicle identification number, title reference, jurisdiction, printed lienholder, issue date. Edit those definitions and choose your required fields before saving. Array fields include defined sub-fields so row values have a validation structure.
What needs review in vehicle title collateral record capture?
The title contains a prior lienholder and a separate release notation. Preserve the printed text and route the record for title review. Check the original source alongside model-reported confidence and validation output; completion status describes the extraction result, not the business decision.
How does this data reach your loan intake worksheet?
Save the schema, then send one supported PDF or image to POST /api/v1/extract with its slug. The response contains the data, model-reported confidence, validation and result status. Your application maps reviewed values to the destination; upload-site submissions can use a configured webhook. No title verification.
How do I test this workflow on my own documents?
Start free with 25 credits per month, 2 schemas, and 1 branded upload site. The starter schema opens in the schema builder after sign-up. Edit the fields, save it, and upload a representative file through your account or the extraction API. Account documents are stored according to Dokyumi’s privacy policy; contact hello@dokyumi.com for deletion requests and retention requirements.