Looking for an Amazon Textract alternative?
A fair, fact-checked look at when to choose an Amazon Textract alternative — per-value source coordinates, an explicit review list, CJK (Japanese/Korean/Chinese) support, a queryable sheet, flat pricing, and no AWS setup.
Amazon Textract is a capable, mature OCR service, and for an AWS-native pipeline processing English documents at scale it's a sensible default. But "capable" and "the right fit for your job" aren't the same thing, and a few real constraints send people looking for an Amazon Textract alternative:
- CJK isn't on the supported list. Textract's printed-text, forms, and tables features cover a set of Latin-script languages (English, French, German, Italian, Portuguese, Spanish); handwriting, invoices/receipts (AnalyzeExpense), IDs (AnalyzeID), and Queries are documented as English-only. Japanese, Korean, and Chinese aren't on that list.
- AWS gravity. Using it means an AWS account, IAM, the SDK, a supported region, and usually S3 — a real setup cost if you just want to send an image and get fields back.
- Feature-stacked, per-page pricing. You pay per page, and the rate depends on which feature you call (plain text vs. forms vs. tables vs. queries vs. expense), with combined features stacking the cost.
- Value-by-value review is a separate service. Textract returns confidence scores; human-in-the-loop review is Amazon Augmented AI (A2I), which you wire up yourself.
This guide is a fair comparison — where Textract is strong, and where an alternative like space-ocr fits.
What to compare when evaluating a Textract alternative
Both tools read documents and return structured data with coordinates. The differences are in how you verify a value, which languages are covered, how the data leaves the tool, and what it costs to start. The table states verified facts for each — use it as a checklist for your own workload.
| Capability | Amazon Textract | space-ocr |
|---|---|---|
| Bounding boxes | Yes — a normalized 0–1 BoundingBox plus a Polygon per block | Yes — per value, a box ({ xmin, ymin, xmax, ymax }) and a four-point quad, both on a 0–1000 normalized grid |
| Per-value verification signal | A recognition confidence (%) per block | verified as the verdict, review.reasons as the ranked list of what tripped, and evidence (text_match, source, match_ratio) as the supporting detail |
| Built-in value-by-value review UI | Not in Textract itself; human review is a separate service (Amazon A2I) | Built into the app — click a cell and its exact region lights up on the original |
| Receipt / invoice fields | AnalyzeExpense (a separate API), English-only | Declare the fields you want with fields, or let autoFields propose a schema — in any language the engine reads |
| Line items | AnalyzeExpense line items (ITEM / QUANTITY / PRICE) | An array field with children; every cell is addressable at its own path (items[0].price) |
| Japanese / Korean / Chinese | Not listed (6 Latin-script languages; Expense/ID/handwriting are English-only) | One engine auto-detects Japanese, Korean, Chinese, English, and more |
| Queryable storage | You store and query results yourself | A stored sheet is queryable server-side via GET /view (where, sort, select) — no re-OCR, no extra charge |
| CSV export | Build it yourself from the JSON | One click — UTF-8 BOM, line items unfolded |
| Pricing model | Per-page, billed per feature; combining features stacks the cost; on top of an AWS account | Flat $0.05 per image; free tier 100 credits/month, no card; Pro $39/month |
| Setup | AWS account + IAM + SDK, regional service, usually S3 | One HTTPS call with a Bearer key; the same API is also an MCP endpoint for AI agents |
About "verifiable": the coordinates aren't taken on the model's word. The language model returns each field's text — and a hint of which word tokens it used — but never the boxes themselves. The engine then character-matches that text against the symbols the vision OCR actually detected on the page, so a box lands on the real pixels those characters were found at. Where that comparison ran, the cell carries evidence.match_ratio for how much of the value was located (0.85 and above is treated as a confident match); where it couldn't run, the key is simply absent. The model's token hints can be noisy — it sometimes swaps them between repeated rows — so column- and row-consistency checks validate them instead of trusting them blindly. The point isn't that the AI can't be wrong; two systems can still agree on the same misread. The point is that silent mismatches get surfaced — as review reasons on the cell, and as a line in data.review.flagged.
Where Textract is the better choice
A fair comparison names where the incumbent wins. Reach for Textract when:
- You're already deep in AWS and want OCR that drops into S3 → Lambda → Textract with IAM and SNS you already operate.
- Your documents are English / Latin-script and you need forms, tables, and queries at very large scale.
- You want custom adapters trained on your own document types, or AWS-native compliance and data-residency guarantees.
If that's you, Textract is a strong fit and an alternative buys you little.
Where space-ocr fits instead
A Textract alternative earns its place when one or more of these matters:
- You process Japanese, Korean, or Chinese documents. space-ocr runs CJK and Latin scripts through one engine with automatic language detection — no language parameter to set.
- You want to verify, not just trust. Business data stays in
data.values, and the same path keys intodata.cells, where thebox,quad,verifiedverdict,reviewreasons, and supportingevidencelive.data.review.flaggedis the resulting work list, and clicking a cell in the app highlights exactly where the value was read. - You don't want to stand up storage. Results land in a sheet you can query server-side (
GET /view) and export to CSV in one click — no database, no AWS account. - You want predictable pricing. A flat $0.05 per image, a free tier of 100 credits a month with no credit card, and a $39/month Pro plan — no per-feature page stacking.
- You build with AI agents. The same API is exposed over MCP at
https://mcp.space-ocr.com/mcp— oneclaude mcp add --transport httpline, or a URL and a bearer header inmcp.json. Nothing to install.
The whole call is one HTTP request — no SDK. The engine reads raster images, so PDF pages are converted to images before they are sent; in the web app that step is handled for you.
curl -s https://api.space-ocr.com/ocr/fields \
-H "Authorization: Bearer $SPACE_OCR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"image": "https://example.com/invoice.jpg",
"imageType": "url",
"fields": [
{ "name": "invoice_no", "type": "string", "required": true },
{ "name": "date", "type": "date" },
{ "name": "items", "type": "array",
"children": [
{ "name": "name", "type": "string" },
{ "name": "qty", "type": "string" },
{ "name": "price", "type": "string" }
] },
{ "name": "total", "type": "number", "required": true }
]
}'Values come back in data.values, shaped exactly like the schema you sent. Every path in that shape — total, items[0].price — keys into data.cells, where a box ({ xmin, ymin, xmax, ymax } on a 0–1000 grid) sits beside a four-point quad that follows a tilted phone photo, plus verified, review, and evidence; data.image gives the width and height those coordinates are measured against. What needs attention is listed once, in data.review.flagged, each entry pairing a path with its reasons. Because date and total were declared with scalar types, a sparse data.normalized tree arrives beside the values with the parsed forms. For the full coordinate model, see an OCR API with bounding boxes; for the async, webhook-driven side, see the invoice data extraction API guide.
Languages: the clearest dividing line
If your documents are Japanese receipts, Korean invoices, or Chinese forms, this is usually the deciding factor. Textract's printed-text, forms, and tables features support six Latin-script languages, and its handwriting, AnalyzeExpense, AnalyzeID, and Queries features are English-only — Japanese, Korean, and Chinese aren't on the supported list. space-ocr normalizes multiple scripts in one engine (full-width and half-width characters, hyphen variants, CJK spacing, vertical Han, mixed scripts), detecting the language automatically with no hint to pass.
Pricing: per-feature pages vs. a flat per-image rate
Textract uses per-page, usage-based pricing where the rate depends on the feature — plain text detection is billed differently from forms, tables, queries, or AnalyzeExpense, and calling several features on one page stacks the cost — all on top of an AWS account. space-ocr is a flat $0.05 per image regardless of how many fields you pull, with a free tier of 100 credits a month and no credit card, and Pro at $39/month for 1,100 credits, unlimited sheets, and 100 GB of storage. Failed extractions aren't charged, and querying a stored sheet (GET /view) is free.
How to try space-ocr as a Textract alternative
- Get a key — no AWS accountSign up for the free tier (100 credits a month, no credit card) and grab your spocr_ API key. There's no IAM, region, or S3 to configure.
- Send the imagePOST the document to /ocr/fields with imageType 'url' or 'base64'. The engine reads raster images, so convert PDF pages to images first; the language is detected automatically.
- Declare your fieldsList what you want in fields — a name and a type for each, with children under an array field for line items — or set autoFields to true and let the model propose the schema from the document.
- Verify each valueFor each path, read data.cells[path]: box and quad for the source region, verified for the verdict, review for the reasons, evidence for the supporting detail. Build the review queue from data.review.flagged rather than from a score threshold. In the app, click a cell to highlight exactly where it was read.
- Query or export — no storage to buildPush images into a sheet with /upload, query it server-side with GET /view (where, sort, select), or download CSV with line items unfolded — no database and no re-OCR charge.