# Confidence scores

Source: https://www.digiparser.com/docs/guides/parsers/parser-settings/confidence-scores

ParsersParser Settings

# Confidence scores

Understand how confidence scores work, what the numbers mean, and when to use them.

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# [Confidence scores](#confidence-scores)

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**Confidence scores** show how sure DigiParser is about each extracted value. They help you quickly see which values are safe to trust and which ones might need a human review.

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When enabled, confidence scores appear:

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*   Next to fields in the **document view**
*   In the **embed preview** when data preview is enabled
*   In API responses as numeric values between **0.0** and **1.0**

## [How confidence scores work](#how-confidence-scores-work)

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When you turn on **Calculate confidence scores for extracted fields**, DigiParser uses a **consensus approach**:

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1.  The document is processed three times using the same schema and the same selected extraction model.
2.  Each run suggests values for every field and table row.
3.  DigiParser **compares all runs field-by-field**, using field descriptions to recognize equivalent formatting or wording.
4.  A final value is chosen for each field, and a **confidence score** is assigned based on how much the runs agree.

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At a high level:

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*   If **all runs agree**, confidence is **1.0** (100%).
*   If **two of three runs agree**, confidence is about **0.67**.
*   If the runs **disagree a lot**, confidence is **lower**.

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If one provider call still fails after retrying, DigiParser can calculate confidence from the two successful runs. At least two successful runs are required because one result cannot measure agreement.

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Tables and arrays are handled row by row:

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*   Rows are **aligned** even if different runs return them in different orders.
*   Each cell (e.g. `Line Items -> Quantity`) gets its **own confidence score**.
*   A row found by only one run is retained so data is not silently lost, but its cells receive a low confidence score.

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You don't need to configure any of this--just enable confidence scores in **Parser Settings** and DigiParser handles the rest.

## [What the scores mean](#what-the-scores-mean)

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Confidence scores always fall between **0.0** and **1.0**. You'll often see them as percentages in the UI (e.g. **82%**).

### [Interpretation guide](#interpretation-guide)

Score

Color in the document view

Suggested action

**0.90-1.00**

Green

Usually a strong result

**0.80-0.89**

Blue

Check important business fields

**0.70-0.79**

Yellow

Review recommended

**0.50-0.69**

Orange

Review carefully

**Below 0.50**

Red

Likely needs correction

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These colors help you prioritize review; they are not a guarantee that a value is correct. Always check important values such as payment totals, dates, and account numbers.

## [Enabling confidence scores](#enabling-confidence-scores)

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To turn on confidence scores for a parser:

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1.  Open your parser and go to **Settings -> Parser Settings**.
2.  Scroll to **Parsing Configuration**.
3.  Find **Calculate confidence scores for extracted fields**.
4.  Turn the toggle **On**.
5.  Click **Save Parser**.

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Once enabled, new documents processed by this parser will include confidence scores. Existing documents can be **re-processed** to add confidence scores.

app.digiparser.com![Parsing Configuration section with Calculate confidence scores toggle enabled](https://documents.digiparser.com/docs/screenshots/2026-07-23/parser-settings-processing-controls.png?sig=88dee9493cc32581aba85341e936e250b59243004ee0ff90637f136c7753d1a2)

### [Credit usage](#credit-usage)

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Confidence scores use **extra compute** because DigiParser runs the extraction three times per page and may use smaller reconciliation calls to compare disagreements.

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Enabling confidence scores adds **+1 credit per page** on top of standard processing:

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*   **Standard processing**: 1 credit per page
*   **With confidence scores enabled**: **2 credits per page** (+1 extra credit per page)

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Using the **Critical** data extraction mode may add more credits. See [Credits and Billing](/docs/guides/troubleshooting/credits-and-billing) for full details.

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When your organization uses its own Gemini or OpenAI API key, these model calls are billed directly by that provider. A confidence-enabled extraction normally includes three full extraction runs plus any smaller reconciliation calls needed for disagreements.

## [Using confidence scores to review documents](#using-confidence-scores-to-review-documents)

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Confidence scores are most useful when you're deciding **what to review first** and **what to send straight through**.

### [In the document view](#in-the-document-view)

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When you open a document in the **split view** (original document on the left, extracted data on the right):

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*   Fields and table cells can show confidence visually (for example, as colors or tooltips).
*   You can hover or click to see the **exact percentage**.

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Use this to:

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*   **Scan for low-confidence values** and fix them directly.
*   Focus on documents that have **many low-confidence fields**.

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See [Check and Correct Extracted Data](/docs/guides/managing-data/editing-extracted-data) for the full review flow.

### [With Reviews & Approvals](#with-reviews--approvals)

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If you use **Reviews & Approvals**, you can build rules that look at confidence:

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*   Flag documents when **overall confidence is low**.
*   Flag documents when a **specific field** has low confidence (e.g. `Total Amount confidence < 0.8`).

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Example rules:

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*   **Confidence < 0.8** - flag documents with confidence below 80%.
*   **Line Items -> Quantity confidence < 0.7** - flag when line item quantities have confidence below 70%.

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Enter review thresholds as decimal values between **0** and **1**. For example, use `0.8` for 80%.

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See [Setting Up Review Stages](/docs/guides/reviews-approvals/setting-up-review-stages) for how to add rules based on confidence.

## [Confidence scores in API responses](#confidence-scores-in-api-responses)

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When you call DigiParser's APIs, confidence information is returned alongside your data.

### [Field-level metadata](#field-level-metadata)

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In data responses, you'll see a `field_metadata` object that mirrors your extracted data:

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*   Each **simple field** (e.g. `email`, `total`) has:
    *   `confidence_score`: number between 0.0 and 1.0
*   Each **array** or **table** has:
    *   An array of metadata objects, one per item/row
    *   Each nested field (e.g. `education[0].degree`, `items[1].qty`) includes its own `confidence_score`

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This structure lets you:

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*   Decide which fields to trust automatically.
*   Build your own review rules outside DigiParser.
*   Store confidence scores in your own systems if needed.

### [Flattened responses](#flattened-responses)

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In some exports and integrations, you'll see confidence values:

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*   As a `confidence` field directly on each extracted field or table cell.
*   As a flattened `confidence_score` object that follows the same shape as your flattened data.

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The numeric meaning is always the same:

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*   `1.0` = all successful runs agreed, including values recognized as semantically equivalent under the field description
*   Lower values = more disagreement across runs

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Confidence measures agreement between independent model runs. It is a review signal, not a guarantee that the selected value is correct.

## [When to use confidence scores](#when-to-use-confidence-scores)

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Confidence scores are especially helpful when:

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*   You process **high-value documents** (e.g. invoices, contracts, payroll).
*   You want to **sample** and review only risky documents instead of checking everything.
*   You're rolling out a new parser and want extra visibility into extraction quality.

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They may be less critical when:

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*   You're processing **low-risk documents**.
*   You already have a strong manual QA process and don't need automated triage.

## [Tips](#tips)

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*   **Start with one parser**: Enable confidence scores on a single parser first and see how they behave on real documents.
*   **Use rules, not just eyeballing**: Combine confidence scores with **Reviews & Approvals** rules to automatically flag risky documents.
*   **Monitor performance**: Confidence scores can increase processing time slightly--enable them where they provide the most value.
*   **Focus on key fields**: Pay the most attention to confidence on critical fields like totals, dates, and identifiers.

## [Next steps](#next-steps)

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*   [Parsing Configuration](/docs/guides/parsers/parser-settings/parsing-configuration) - Other parsing options, including pages and file types
*   [Check and Correct Extracted Data](/docs/guides/managing-data/editing-extracted-data) - How to review and correct extracted values
*   [Setting Up Review Stages](/docs/guides/reviews-approvals/setting-up-review-stages) - Flag documents based on confidence and other rules
*   [Credits and Billing](/docs/guides/troubleshooting/credits-and-billing) - How credits are calculated

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