Introducing our Perfect Docs Guaranteed offer — 99%+ accuracy for high-volume teams. Limited spots available. Learn more
By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
See how we evaluate, normalize, and validate data extraction accuracy across millions of documents using a transparent, ground-truth benchmarking process built for real-world evals.
Trusted by 100+ companies to validate document parsing and data extraction accuracy.
Ground Truth: The Starting Point for Measuring Accuracy
Accurate benchmarking starts with an accurate baseline — what we call the ground truth.
It defines the correct data for every field in a document, allowing us to measure extraction accuracy objectively across your processing pipeline.
When a customer shares their labeled data, we use it as the standard reference. If not, Invofox helps define it so the comparisons are consistent and reproducible.
Document
Ground Truth
Comparison
How Invofox Handles Complex Data in Accuracy Evaluation
Document data rarely looks identical, even when it’s correct. Our accuracy evaluation logic adapts to each data type to ensure comparisons remain fair and consistent:
Numbers
Compared within tolerance ranges
Dates
Standardized to avoid time-zone mismatches.
Booleans
Account for missing or unchecked states.
Arrays and Tables
Evaluated by content, not order (unless order is business-critical).
Texts/Strings
Compared at three levels:
Exact Match: For critical fields like totals, IDs, or contract numbers, values must be 100% identical.
Normalized Match: Formatting differences (case, spaces, punctuation) are cleaned before comparison to avoid false mismatches.
Similarity Match (Levenshtein Distance): For flexible fields like names or addresses, we calculate how similar two strings are on a scale from 0–1.
Numbers
Compared within tolerance ranges
Dates
Standardized to avoid time-zone mismatches.
Booleans
Account for missing or unchecked states.
Arrays and Tables
Evaluated by content, not order (unless order is business-critical).
Texts/Strings
Compared at three levels:
Exact Match: For critical fields like totals, IDs, or contract numbers, values must be 100% identical.
Normalized Match: Formatting differences (case, spaces, punctuation) are cleaned before comparison to avoid false mismatches.
Similarity Match (Levenshtein Distance): For flexible fields like names or addresses, we calculate how similar two strings are on a scale from 0–1.
From Field Accuracy to Full-Document Reliability
Most Intelligent Document Processing (IDP) vendors report only field-level accuracy (how many individual fields are correct).
Invofox goes further by measuring document-level accuracy, which reflects how many documents are fully correct and ready for automation.
Because even a single wrong field can stop a workflow, we calculate both: ·Per-field accuracy: granular precision for analytics ·Per-document accuracy: end-to-end reliability for automation
In addition, we apply custom validation rules tailored to each use case, ensuring accuracy reflects your specific data and workflow requirements.
This approach ensures your performance metrics match your operational reality.
Want to See How Your Vendor Compares?
Benchmark your current IDP or in-house system against Invofox — we’ll show you the data side by side.
Maintaining Benchmark Consistency When Your Schema Evolves
Adding or removing a field can make old benchmarks impossible to compare.
Invofox tracks schema versions and normalizes changes automatically, so your accuracy results remain valid over time.
When new keys are introduced, we flag affected documents to help you identify what’s changed and maintain clear visibility into your evolving data model.
Accuracy Evaluation Built on Transparency
We believe accuracy metrics should be verifiable, not subjective.
That’s why Invofox runs every eval in-house, using consistent parameters and transparent rules.
Each customer receives both summary metrics and the raw data used to calculate them — no black boxes, no hidden assumptions.
When clients share live feedback, Invofox applies the same evaluation criteria in real time, continuously computing our metrics and refining the models to improve accuracy.
Field
Client Accuracy
Client False Positives
Invofox Accuracy
Invofox False Positives
Document Number
89.4%
5.4%
99.3%
0%
Tax Base Amount
87.9%
3.8%
98.8%
0%
OrderRef
88.7%
6.2%
99.1%
0%
Frequently Asked Questions about Accuracy Evaluation
What happens if we change our schema mid-test?
Can we access the raw benchmark results?
How do you ensure fairness when comparing vendors?
What industries or document types is this best for?
