OCR Accuracy Comparison — Textract vs Document AI vs ParseFlow

Accuracy is the most important metric for any OCR API. A 1% difference in character error rate can mean thousands of misread documents at scale. Here is how the major players compare.

What We Measure

• Character Error Rate (CER) — percentage of misread characters
• Field Extraction Accuracy — percentage of correctly extracted key-value pairs
• Table Structure Accuracy — percentage of correctly reconstructed table cells
• Handwriting Recognition — accuracy on cursive and printed handwriting

General OCR Accuracy

On clean, typed documents (invoices, forms, contracts), all three APIs achieve 98–99%+ character accuracy. The differences appear on challenging documents:

• Low-quality scans: ParseFlow (Gemini 2.5 Flash) handles skewed, low-light, and crumpled documents well due to the vision model's training on diverse real-world images.
• Handwritten text: Google Document AI has dedicated handwriting models that perform better on cursive text. ParseFlow handles block-print handwriting but cursive has higher error rates.
• Complex layouts: Multi-column documents, nested tables, and mixed content (text + forms + tables) — ParseFlow excels here because the vision model processes the entire page holistically.

Field Extraction Accuracy

For structured field extraction (invoice number, date, total amount):

Try ParseFlow yourself with your own documents to see how it performs on your specific use case.