The Best OCR APIs for Spanish-Language Documents (2026)

For reading Spanish text, almost any modern engine works: Google, Azure and Tesseract all recognize it well. The real difference comes afterwards: extracting custom fields without training models, understanding Spanish invoices with IVA and IRPF, handling NIF and CIF identifiers, and processing data with EU residency under GDPR. That is where this comparison is decided.

We evaluated seven tools against those criteria. Here is the full picture:

Quick comparison

Tool by tool.

anyformat

anyformat is a European document intelligence platform for teams processing invoices, payslips and contracts, and the only vendor in this comparison with a product and documentation fully available in Spanish. It extracts custom fields zero-shot: define your schema (supplier, tax base, IVA, IRPF withholding, NIF) and it works on the first document, with no labeling or training. Every returned value carries a visual citation linking it to its exact position on the page and a calibrated confidence score that decides what goes to human review. In a public benchmark on 1,000+ real documents it reaches a 78.1% Parse Score. EU-native, ISO 27001 certified, with on-premise deployment and a no-code workflow Studio.

The second half of the product is measurement. Every Extract and Classify workflow has a Health tab: build a dataset with verified ground truth (promoted from reviewed production files or uploaded as labelled data), tag sub-datasets to slice accuracy by supplier, document type or difficulty, and run numbered, immutable evaluations that score any workflow version field by field against that ground truth, with result-versus-expected comparisons. The Health Overview puts the dataset benchmark next to live production accuracy, confidence and through rate. That matters in Spanish-market processes where a supplier changes its invoice layout and nobody notices until the ERP reconciliation fails. anyformat has also announced Optimizer, a workflow that tunes itself against its own dataset using your evals as the target; it is not shipped yet.

Pricing is credit-based, charged per page and per operator (10 credits = €0.01): Parse 25 credits per page, Extract 35, Classify 10, Split 25, with agentic variants at 100 and 150. The $25 per 1,000 pages quoted in the benchmark is the parsing rate.

Best for: Extracting custom fields from Spanish invoices, payslips and contracts without a labeling project, with human review and accuracy measurement in the same platform.

Google Document AI

Google Document AI is Google Cloud's document processing platform, built for high-volume OCR inside the GCP ecosystem. Its Enterprise Document OCR recognizes text in 200+ languages according to the official documentation, Spanish included, and it is among the cheapest engines per page. Custom fields no longer require a labeling project: the Custom Extractor runs on a foundation model that extracts from a schema alone, with few-shot accepting up to 5 labeled examples and fine-tuning requiring 50 training plus 50 test documents to reach production accuracy. The limits sit elsewhere: many online processing requests are capped at 15 pages, deployed custom processor versions carry hosting fees, and data lives in configurable GCP regions under US jurisdiction.

Best for: raw multilingual OCR at scale in stacks that already live on GCP.

Azure Document Intelligence

Azure Document Intelligence (formerly Form Recognizer) is Microsoft's offering, with solid pre-trained models and tight integration with the rest of Azure. It is one of the few US options with explicit Spanish support for invoices: the official documentation lists Spain Spanish among the languages of its prebuilt-invoice model. For fields that model does not cover (an IRPF withholding, an internal reference) you have to label documents in Document Studio, five examples minimum per Microsoft's documentation and usually more, and train a custom model, with a cycle for every schema change. Container deployment exists but covers only a subset of features.

Best for: teams in the Microsoft ecosystem whose fields match the pre-trained models.

AWS Textract

AWS Textract is Amazon's OCR service, with a strong reputation for table and form extraction. With Spanish documents you need to read the fine print: according to its own FAQ, Textract extracts printed text, forms and tables in English, German, French, Spanish, Italian and Portuguese, but handwriting recognition, invoice and receipt analysis (AnalyzeExpense), identity documents and Queries work in English only. In other words: the feature built specifically for invoices does not work on a Spanish invoice. It returns raw OCR (text and coordinates), so your team builds the structure with post-processing. Combined forms and tables analysis is $65 per 1,000 pages, $70 with queries added (AWS pricing, 2026).

Best for: AWS pipelines that need tables from printed documents and can build the extraction layer themselves.

ABBYY

ABBYY is the industry veteran: an IDP vendor founded in 1989, with the classic FineReader as its multilingual OCR engine (Spanish included) and two main products, Vantage (cloud) and FlexiCapture (on-premise). Its library of 150+ pre-built Skills covers many document types; anything outside it requires custom training and, frequently, professional services. As we cover in our detailed comparison, the delivery model weighs more than the technology: implementations of weeks to months with third-party integrators, services budgets from $15K to over $200K, and multi-year contracts.

Best for: large corporations with stable document types and the budget and timeline for a traditional deployment.

Tesseract

Tesseract is the reference open-source OCR engine, Apache 2.0 licensed, with official training data for 100+ languages and 35+ scripts according to its documentation, and a Spanish package (spa) that performs well on clean printed text. It is free and runs wherever you decide, so data residency is solved by definition: documents never leave your server. Let's be clear about what it is: raw OCR. It returns plain text, not fields; quality drops on skewed scans, phone photos or handwriting, and everything else (classification, extraction, validation, review, accuracy monitoring) is built by your team.

Best for: projects with a technical team and zero budget that only need to turn images into text.

Nanonets

Nanonets is a mid-market IDP platform founded in 2017, with a visual no-code workflow builder and template-free extraction. Its own documentation acknowledges that out-of-box models may require human-in-the-loop (HITL) annotation to reach production accuracy on non-standard documents, which in practice means labeling data. Pricing is per block run ($0.02–$0.30) and compounds when a workflow chains several blocks; Nanonets' own pricing page puts a typical invoice workflow at four to six block runs per document. Data lives by default on US multi-tenant infrastructure; on-premise exists, but only through an enterprise contract.

Best for: SMBs without strict sovereignty requirements looking for simple automation.

What to look for when choosing OCR for Spanish documents

Reading the text well is the easy part. These six criteria are what separate a pilot from a production system:

1. GDPR and EU data residency. A configurable European region on a US hyperscaler is not the same as a European vendor: the jurisdiction remains American. If your sector (banking, healthcare, public administration) demands real sovereignty, filter for EU vendors or on-premise deployment before looking at prices.

2. Spanish-market document types. Invoices with itemized IVA, IRPF withholding or equivalence surcharge; payslips with social security contribution bases; deeds and contracts; tax forms. Check that the system handles NIF, CIF and NIE identifiers, DD/MM/YYYY dates and amounts with decimal commas (1.234,56 €) without normalizing them to US formats.

3. Custom fields without training. Pre-trained models cover the standard fields of an invoice, not yours. If every schema change requires labeling documents and retraining, that is where the real project cost lives. Zero-shot extraction removes that cycle: define the field and it works on the first document. anyformat and Google's Custom Extractor both work this way; Azure, ABBYY and Nanonets still involve labeling.

4. Human review and confidence. No system is always right. The question is whether it warns you when it is unsure: calibrated per-field confidence lets you auto-approve what is certain and route only doubtful values to review, and visual citations let the reviewer (or the auditor) check every value against the original page.

5. A way to measure accuracy over time. Confidence tells you what to review today; it does not tell you whether last week's workflow change made things worse. A dataset with verified ground truth plus repeatable, versioned evaluations does. Ask each vendor how you compare two workflow versions field by field, and how you see the dataset benchmark next to live production accuracy.

6. The pipeline you would otherwise build. A raw OCR API returns text and coordinates. Post-processing, schema validation, retry and routing logic, a review interface, accuracy monitoring and re-tuning every time a supplier changes its invoice layout are yours to build and maintain. Weigh that engineering against a platform that ships it, because the per-page price is rarely the deciding number.

Frequently asked questions

If you are evaluating specific vendors for Spanish-language documents, we have published dimension-by-dimension comparisons: start with anyformat vs Google Document AI.