OCR Accuracy Comparison: OCR Benchmark Results and the Most Accurate OCR Software for US Documents

To be fair to all three: this is a defensible position, not a dodge. Accuracy on documents depends so heavily on scan quality, layout, language and document type that one headline figure would be misleading in most directions at once. But it does have a consequence buyers should price in. If a vendor publishes no number and joins no benchmark, there is nothing to hold them to, and the entire burden of evaluation lands on you. The vendors that do publish, mostly the open-weight projects, are easier to check and easier to hold to account. Cost, at least, is knowable in advance, and we normalize it across every vendor on the OCR API pricing comparison.

How accurate is OCR?

On the current independent leaderboard the best document models land near 96 on a 100-point composite, and the weakest widely used tools sit in the high 70s. But that composite blends text edit distance with table and formula structure scores, so it is not a claim that 96 percent of characters were read correctly. On clean, typed, single-column English pages, good OCR is very reliable. On dense tables, handwriting and poor scans, the spread between tools is enormous.

Which OCR is most accurate?

On OmniDocBench v1.7, the highest scoring entry is PaddleOCR-VL-1.6 at 96.34 Overall, ahead of MinerU2.5-Pro at 95.75 and GLM-OCR at 95.22. All three are open-weight specialized models. The highest scoring commercial API on that board is Gemini 3 Pro at 92.91. AWS Textract, Azure Document Intelligence and Google Document AI are not on the board at all.

Do AWS, Azure and Google publish OCR accuracy numbers?

No. We checked their own documentation on 2026-08-21 rather than assuming. The word "accuracy" appears twice on the AWS Textract FAQ page, once on the Azure Document Intelligence overview and not at all on the Google Document AI overview, and in no case is it attached to a percentage. None of the three appears anywhere in the OmniDocBench leaderboard either.

What is a good OCR accuracy rate?

For production document workflows, the useful question is not the character rate but the field rate: how many of the values you actually extract are correct without a human touching them. Teams running invoice or statement processing generally want straight-through processing on most documents with the remainder flagged for review by confidence score. A 99 percent character rate can still mean a wrong total on every page.

How do I evaluate the accuracy of OCR on my own documents?

Build a small labeled set of 50 to 200 real pages that reflects your worst cases, not your cleanest ones. Score the fields you care about, not the whole page. Run every candidate on identical inputs on the same day. Then measure the review cost: what matters commercially is how many documents a person has to open, which is a function of both accuracy and whether the tool tells you when it is unsure.

Is a bigger model more accurate at OCR?

The benchmark says no. Qwen3-VL-235B scores 89.78 Overall, below OpenDoc-0.1B at 90.67 despite being roughly 2,350 times larger. Kimi K2.5 at a trillion parameters scores 84.53, more than six points below that same 0.1B model. Document parsing rewards models trained on document structure, not raw scale.

Are general AI models like GPT and Gemini accurate enough for OCR?

They are competitive but not leading. Gemini 3 Pro scores 92.91 and GPT-5.2 scores 86.59, against 96.34 for the top specialized model. The gap widens on tables, where GPT-5.2 scores 82.95 and the leader scores 94.76. For reading a handful of documents that is fine. For a pipeline processing thousands, the structure gap is where the review hours come from.

Does OCR accuracy differ by language?

Substantially, and the published headline is usually the vendor's strongest language. PaddleOCR's own table shows its PP-OCRv5 server recognition model at 86.38 on Chinese but 64.70 on English, a gap of nearly 22 points on the same model. Its dedicated English model scores 85.25. If you are reading US documents, check that you are looking at the English column and running the English model.

Why do OCR vendors quote 99 percent accuracy?

Because character-level accuracy on clean printed text genuinely is around that, and it is the most flattering way to state the number. It says nothing about whether the right value landed in the right field, whether a merged table cell shifted a column, or whether the total was read off the wrong line. Field-level accuracy on real documents is always lower than character-level accuracy on clean ones.

How is OCR accuracy measured?

Character and word error rates compare output text to a ground truth string. Document parsing benchmarks go further: OmniDocBench combines a normalized text edit distance with TEDS for table structure and CDM for formulas, then averages the three. Different benchmarks use different formulas, which is exactly why scores from two different leaderboards cannot be compared directly.

Can I improve OCR accuracy without changing vendors?

Often, yes, and it is usually the cheapest win available. Send higher resolution scans, deskew and de-noise before upload, pick the language-specific model rather than the multilingual default, and use the structure-aware operation instead of plain text extraction when the layout carries meaning. On most clouds that last change moves you to a more expensive meter, which is why it gets skipped.

What accuracy does DocuOCR claim?

We do not publish a leaderboard score, and we would treat any vendor number, ours included, as marketing until you reproduce it. Extraction here runs with per-field confidence scores and a review step, so low-confidence values surface instead of silently landing in your system. The number that should decide your project is the one you measure on your own documents.