AWS Textract vs Google Vision (and When to Skip Both)

The first time I had to choose an OCR service for a document pipeline, I spent a week benchmarking accuracy. Then I watched the whole decision get made in a single meeting by someone from legal who asked where the patient data would be processed. Accuracy barely came up. That meeting reshaped how I evaluate OCR: the question that decides a project is usually not "which engine reads better," it's "what shape are the documents, where is the data allowed to live, and how does the bill behave as volume grows."

Full transparency: I'm a developer advocate at Iron Software, and one of the three options here (IronOCR) is ours. The other two are cloud services we don't sell. I'll keep this grounded in real code and call out where AWS Textract or Google Cloud Vision is the better pick, because for a lot of workloads one of them is exactly right. Judge the code and the trade-offs for yourself.

AWS Textract

Textract is built for structured documents. Beyond returning a wall of text, its Analyze Document API extracts key-value pairs, reconstructs tables with rows and columns intact, detects signatures, and supports queries like "what is the total due?" There are purpose-built APIs for expenses, identity documents, and lending packages. If your core problem is pulling fields out of standardized paperwork, that erases a lot of custom parsing code you'd otherwise own forever.

In .NET you call it through the AWS SDK. The minimal text-detection call looks like this:

using Amazon.Textract;
using Amazon.Textract.Model;

var client = new AmazonTextractClient();

// Read the file into memory; Textract accepts raw bytes for synchronous calls.
var request = new DetectDocumentTextRequest
{
    Document = new Document { Bytes = await File.ReadAllBytesAsync("doc.png") }
};

DetectDocumentTextResponse response = await client.DetectDocumentTextAsync(request);

// Blocks come back typed; LINE blocks hold the reading-order text.
foreach (var block in response.Blocks)
{
    if (block.BlockType == BlockType.LINE)
        Console.WriteLine(block.Text);
}

That prints each detected line of text in reading order. The trade-offs I'd weigh: Textract's OCR is tuned for a focused set of Latin-script languages (English, Spanish, German, Italian, French, Portuguese), so a global multi-script corpus isn't its strength. Pricing is per page and per API, so text, tables, and forms are billed separately, and a document that needs forms plus tables gets charged for both. And the document leaves your machine for AWS to process it. For an English-or-European invoice workflow already running on AWS, none of that is a dealbreaker. Confirm current details on the Textract FAQ and pricing page.

Google Cloud Vision

Vision is built around broad language reach. Its OCR covers 80+ languages, and the recommended practice is to leave the language hint empty so the service auto-detects the script. That makes it a natural fit for mixed-language archives, international receipts, or user-submitted photos where the language isn't known ahead of time. It exposes TEXT_DETECTION for short strings in natural images and DOCUMENT_TEXT_DETECTION for dense pages and handwriting, returning a structured hierarchy of pages, blocks, paragraphs, words, and characters with bounding boxes.

The .NET client library keeps the call short:

using Google.Cloud.Vision.V1;

var client = ImageAnnotatorClient.Create();
var image = Image.FromFile("doc.png");

// DetectDocumentText is the dense-page mode; it also handles handwriting.
TextAnnotation result = client.DetectDocumentText(image);
Console.WriteLine(result.Text);

That prints the full extracted text as one string, with the structured hierarchy available on result when you need geometry. Where Vision stops is form semantics: it returns text geometry, but it does not natively pair a label with its value or rebuild a table as a table. Applications that need that from Google typically add the separate Document AI product or build the logic themselves. Like Textract, it's cloud-only and metered per unit (the first 1,000 units each month are free, with a tiered rate after), and requesting both detection features on one image counts as two units. Current rates and supported languages live on the Cloud Vision OCR docs and pricing page.

IronOCR

IronOCR is a commercial .NET library built on a tuned Tesseract 5 engine that runs entirely inside your own process. It reads images and PDFs and returns text and structured output without sending any data to an external service, which is the whole point: no REST client, no API key, no per-call billing, and it works offline.

using IronOcr;

// Runs in-process. No network call, no document leaves this machine.
var result = new IronTesseract().Read(new OcrInput("doc.png"));
Console.WriteLine(result.Text);

That prints the recognized text, and installation is a single Install-Package IronOcr. It supports 125+ languages through downloadable packs and returns structured results down to pages, blocks, lines, and words for layout-aware work. Because the engine runs locally, the same code runs identically on a developer laptop, a build server, or an air-gapped production host. There's no endpoint to be unreachable.

I'll be honest about where IronOCR is weaker. It does not match Textract's purpose-built form and table extraction, and its handwriting recognition is tuned for printed text rather than free handwriting. If the core problem is parsing handwritten forms in volume, a cloud service is the better tool. What you get in exchange is data residency, predictable licensing, and offline operation.

How they compare

Three options is enough to blur together, so here they are side by side. The row that ends most evaluations isn't accuracy, it's data privacy.

Factor	AWS Textract	Google Cloud Vision	IronOCR
Pricing	Per page, per API	Per unit, tiered after free tier	Perpetual license, no per-call fees
Languages	Focused Latin-script set	80+ with auto-detect	125+ via Tesseract 5
Forms & tables	Strong, dedicated features	Text geometry only	Structured output, no form pairing
Deployment	Cloud-only (AWS)	Cloud-only (Google)	In-process, on your server
Data privacy	Sent to AWS	Sent to Google	Never leaves your machine
Offline use	No	No	Yes

The two cloud services compete most directly on accuracy and managed scale, with continuously updated models a local library can't match on the hardest inputs. The on-prem column doesn't try to win that fight; it competes on residency, predictability, and offline capability. For healthcare records, legal discovery, documents under strict residency rules, or any system inside an air-gapped network, sending the document to a third party is either prohibited or operationally impossible, and that's the gap a local library fills.

So which one?

There's no single winner here, and I'd be suspicious of any comparison that claimed otherwise.

• Pick AWS Textract if your documents are structured (invoices, IDs, receipts), the value is in key-value pairs and tables, and you're already on AWS.
• Pick Google Cloud Vision if your documents span many languages, include handwriting, or arrive as everyday photos where auto-detection matters more than form structure.
• Pick IronOCR if the data can't leave your network, you need offline or air-gapped operation, or you want a fixed cost with no per-page cloud fees inside a native .NET app.

A hybrid is also reasonable: handle the bulk of documents locally for residency and cost, and route the small share of complex forms to a cloud service. The fairest test is your own data, so run the same batch of representative documents through all three and compare the text output, the cost, and where the data ended up.

Which OCR are you running in production, cloud or on-prem, and what made you pick it? I'd genuinely like to hear what won on your documents, and whether it was accuracy or something around it that decided things.

If you want to benchmark the on-prem option against your own files, IronOCR has a free trial.

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AWS, Amazon Textract, Google Cloud, and Google Cloud Vision are trademarks of their respective owners. This comparison reflects publicly available information at the time of writing and is provided for informational purposes only.