Compare document extraction accuracy using AI models

A checked answer makes a better test. Illustrative example.

Plan an AI document extraction benchmark for Dynamite Docs. Use reviewed test data to compare OCR, field and table errors, costs, and correction time.

Choose what to measure

Text recognition, model behavior and structured field quality answer different questions. Use the same reviewed documents to connect the results.

Character error rate / word error rate

Measure OCR accuracy using AI vision models

Learn how to measure AI OCR accuracy with Dynamite Docs. Compare extracted text with a reviewed transcript using character and word error rates.

Model comparison / repeated runs

Benchmark field extraction using LLMs

Learn how to benchmark LLM document extraction with Dynamite Docs. Compare field errors, failed runs, latency, costs, and correction time on the same files.

Precision / recall / F1

Test table extraction accuracy with AI across complex layouts

Learn how to test AI table extraction with Dynamite Docs. Use reviewed fixtures and repeatable scoring to check row counts, columns, and page breaks.

Apply the test in Dynamite Docs

Choose a document workflow, run your sample, and compare the result with your reviewed answer.

Invoices
Check identifiers, tax, totals and line items.
Receipts
Test faint text, tips, dates and merchant names.
Bank statements
Check transaction coverage, signs and balances.
PDFs
Separate text-layer, scanned and mixed-page results.
API
Retain outputs and track failures across repeatable runs.
PDF tables
Check table boundaries, page breaks, row counts and totals.
Statement to Excel
Test transaction coverage, signs and balance reconciliation.
BUSY invoices
Verify GSTIN, HSN or SAC, tax splits and bill totals.

Start with a ground-truth test set

Choose 10 to 30 representative documents, including clean files, unusual layouts, scans, and photos.
Record the correct fields, line items, totals, and row counts in a reviewed spreadsheet.
Run unchanged copies through Local (no AI) and each LLM using the same page selection and schema.
Count exact field matches, missing rows, wrong totals, correction time, and changes between repeated runs.

Test Local (no AI)

The deterministic code path works with CSV, TSV, XLSX, and clean text-layer documents that contain a clear table or label-value structure. Choose Local (no AI), run the full test set, count unsupported files as failures, and rerun to confirm that the result stays identical.

Test an LLM model

Open AI Settings, choose All Models, run the compatibility test, then select a passing model. Process the same files and repeat the run. Scanned pages require a vision-capable model. The built-in compatibility test checks whether the API and image request work; it does not measure accuracy.

Read confidence scores

Data Studio shows overall confidence and a badge for each field. Overall scores from 80 to 100 indicate a higher-confidence result, 50 to 79 indicate moderate confidence, and scores below 50 require close review. Field badges use bands of 70 and above, 40 to 69, and below 40.

Confidence is a review signal, not measured accuracy or a guarantee. Compare identifiers, dates, money, and low-confidence fields with the source document before export.

Know when to choose another model

Switch when the same required field fails across several files, a scanned document needs stronger vision support, or another model scores better on the fixed test set. Fix the input instead when blur, cropping, or glare makes the source unreadable. Keep the current model when it meets your error limit and correction-time target.

Run a repeatable document extraction benchmark

Compare models using the same documents and reviewed answers. Check field accuracy, missing rows, totals, processing time, and the corrections each result needs before you choose a model for everyday work.

Run a test document or open model settings.