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Extracting data from foreclosure judgments

In judicial foreclosure states, a county court signs a judgment at the end of each case, often on a draft prepared by the lender's law firm. A servicer's default team receives one for every loan foreclosed there, as a scanned PDF from foreclosure counsel or a download from the court docket.

Default servicing needs the case number, the total award with its principal and interest, the fees and the post-judgment rate. The default servicing system records them, sets the credit bid for the sheriff's sale and accrues interest until the sale.

Page 3 of a sample Foreclosure Judgment, with extracted values outlined
10,746Markdown characters
125parse blocks
34fields extracted
The problem

What makes foreclosure judgments hard

The award is a scanned table

The award splits the debt into principal, interest, late charges and fees in a ruled table, often running across a page break.

Each drafter has a format

Florida circuits use a uniform final judgment form, while elsewhere the lender's law firm drafts its own, with its file number in the footer.

Findings written as prose

Interest rates, the deed of trust's recording details and whether a deficiency is awarded sit in numbered paragraphs of findings and orders.

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 foreclosure judgment, end to end

About this document: A general judgment of foreclosure and sale from the Multnomah County Circuit Court in Oregon, with its certificate of readiness, posted in a sheriff's sale packet. Six scanned portrait pages on line-numbered pleading paper: the caption, numbered findings and orders, the award and fee tables, and the judge's signature. Page 3 is featured. The borrower, defendants, property and file numbers were replaced with stand-ins.

Processing objective: The objective is to lift the money award off a scanned page, one cell at a time. Each amount grounds to its own cell. The prejudgment interest rate is read from inside the row label that states it, beside the per diem that accrues until judgment.

Demonstration fields: Principal, Prejudgment interest, Prejudgment interest rate (%), Late charges, Other costs and fees

The document

4 of 6 pages
  • Page 1 of the sample Foreclosure JudgmentPage 1
  • Page 2 of the sample Foreclosure JudgmentPage 2
  • Page 3 of the sample Foreclosure JudgmentPage 3shown below
  • Page 4 of the sample Foreclosure JudgmentPage 4

Where each value came from

Page 3 of the sample Foreclosure Judgment, with every featured field outlined
Principalamounts_due.principal
352,970.28
The region of the sample Foreclosure Judgment containing Principal, with the extracted value outlined
extraction_metadata["amounts_due.principal"]
{
  "value": 352970.28,
  "page": 3,
  "range": {
    "end": 4129,
    "start": 4118
  },
  "box": {
    "xmin": 0.53395,
    "ymin": 0.58135,
    "xmax": 0.71509,
    "ymax": 0.59952
  }
}
Prejudgment interestamounts_due.prejudgment_interest
59,253.99
The region of the sample Foreclosure Judgment containing Prejudgment interest, with the extracted value outlined
extraction_metadata["amounts_due.prejudgment_interest"]
{
  "value": 59253.99,
  "page": 3,
  "range": {
    "end": 4325,
    "start": 4315
  },
  "box": {
    "xmin": 0.71507,
    "ymin": 0.59753,
    "xmax": 0.85985,
    "ymax": 0.66542
  }
}
Prejudgment interest rate (%)amounts_due.prejudgment_interest_rate_percent
3.875
The region of the sample Foreclosure Judgment containing Prejudgment interest rate (%), with the extracted value outlined
extraction_metadata["amounts_due.prejudgment_interest_rate_percent"]
{
  "value": 3.875,
  "page": 3,
  "range": {
    "end": 4297,
    "start": 4169
  },
  "box": {
    "xmin": 0.18015,
    "ymin": 0.59952,
    "xmax": 0.53407,
    "ymax": 0.66852
  }
}
Late chargesamounts_due.late_charges
98.6
The region of the sample Foreclosure Judgment containing Late charges, with the extracted value outlined
extraction_metadata["amounts_due.late_charges"]
{
  "value": 98.6,
  "page": 3,
  "range": {
    "end": 4383,
    "start": 4377
  },
  "box": {
    "xmin": 0.53407,
    "ymin": 0.66542,
    "xmax": 0.71507,
    "ymax": 0.68343
  }
}
Other costs and feesamounts_due.other_costs_and_fees
31,214.73
The region of the sample Foreclosure Judgment containing Other costs and fees, with the extracted value outlined
extraction_metadata["amounts_due.other_costs_and_fees"]
{
  "value": 31214.73,
  "page": 3,
  "range": {
    "end": 4475,
    "start": 4466
  },
  "box": {
    "xmin": 0.53409,
    "ymin": 0.68237,
    "xmax": 0.71507,
    "ymax": 0.70051
  }
}

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
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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.

API-first

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.

Affordable

Cost-competitive rates with complete price transparency. Spend controls before you run, and every job reports its exact cost.

Enterprise-grade

Built for production and scale from day one. Fully observable and monitored, running securely in our cloud or yours.

Built for high-volume, high-throughput production

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Capabilities

Foreclosure Judgment processing highlights

Award tables from scans
Principal, interest and fees
Negative credits as numbers
Interest rates and per diem
Post-judgment interest rate
Case number and court
Deed of trust recording details
Deficiency and homestead findings
Judge's signature detection
Tables across page breaks
Line-numbered pleading paper
Bounding box for every value
FAQs

Questions, answered

Yes. Parsing works from the page image, so a scanned table comes back with its rows and cells intact. Each extracted amount grounds to the cell that printed it, including tables that continue across a page break.

An amount printed in parentheses or with a minus sign comes back as a negative number when the field is typed as a number. Grounding still points at the figure as the court's document printed it.

Yes. Describe each finding in your schema, such as whether a deficiency judgment is awarded or the post-judgment interest rate. Extraction reads the numbered paragraphs and returns each as a boolean, a number or a date.

No. A schema names the fields you want and describes them in plain language. Extraction reads the parsed judgment for meaning, so one schema covers each court's form and each firm's own drafting.

Every extracted value carries the character range it was quoted from in the parsed Markdown. The Ground API maps that range to a page and a bounding box, so a reviewer sees each amount in the cell that printed it.

Start extracting foreclosure judgment data

Reliable, structured outputs with full traceability in minutes