Optical character recognition has moved from plain text extraction to document intelligence. Modern systems must read scanned and digital PDFs in one pass, preserve layout, detect tables, extract key value pairs, and work with more than one language. Many teams now also want OCR that can feed RAG and agent pipelines directly. In 2025, 6 systems cover most real workloads:
- Google Cloud Document AI, Enterprise Document OCR
- Amazon Textract
- Microsoft Azure AI Document Intelligence
- ABBYY FineReader Engine and FlexiCapture
- PaddleOCR 3.0
- DeepSeek OCR, Contexts Optical Compression
The goal of this comparison is not to rank them on a single metric, because they target different constraints. The goal is to show which system to use for a given document volume, deployment model, language set, and downstream AI stack.
Evaluation dimensions
We compare on 6 stable dimensions:
- Core OCR quality on scanned, photographed and digital PDFs.
- Layout and structure tables, key value pairs, selection marks, reading order.
- Language and handwriting coverage.
- Deployment model fully managed, container, on premises, self hosted.
- Integration with LLM, RAG and IDP tools.
- Cost at scale.
1. Google Cloud Document AI, Enterprise Document OCR
Google’s Enterprise Document OCR takes PDFs and images, whether scanned or digital, and returns text with layout, tables, key value pairs and selection marks. It also exposes handwriting recognition in 50 languages and can detect math and font style. This matters for financial statements, educational forms and archives. Output is structured JSON that can be sent to Vertex AI or any RAG system.
Strengths
- High quality OCR on business documents.
- Strong layout graph and table detection.
- One pipeline for digital and scanned PDFs, which keeps ingestion simple.
- Enterprise grade, with IAM and data residency.
Limits
- It is a metered Google Cloud service.
- Custom document types still require configuration.
Use when your data is already on Google Cloud or when you must preserve layout for a later LLM stage.
Textract provides two API lanes, synchronous for small documents and asynchronous for large multipage PDFs. It extracts text, tables, forms, signatures and returns them as blocks with relationships. AnalyzeDocument in 2025 can also answer queries over the page which simplifies invoice or claim extraction. The integration with S3, Lambda and Step Functions makes it easy to turn Textract into an ingestion pipeline.
Strengths
- Reliable table and key value extraction for receipts, invoices and insurance forms.
- Clear sync and batch processing model.
- Tight AWS integration, good for serverless and IDP on S3.
Limits
- Image quality has a visible effect, so camera uploads may need preprocessing.
- Customization is more limited than Azure custom models.
- Locked to AWS.
Use when the workload is already in AWS and you need structured JSON out of the box.
3. Microsoft Azure AI Document Intelligence
Azure’s service, renamed from Form Recognizer, combines OCR, generic layout, prebuilt models and custom neural or template models. The 2025 release added layout and read containers, so enterprises can run the same model on premises. The layout model extracts text, tables, selection marks and document structure and is designed for further processing by LLMs.
Strengths
- Best in class custom document models for line of business forms.
- Containers for hybrid and air gapped deployments.
- Prebuilt models for invoices, receipts and identity documents.
- Clean JSON output.
Limits
- Accuracy on some non English documents can still be slightly behind ABBYY.
- Pricing and throughput must be planned because it is still a cloud first product.
Use when you need to teach the system your own templates or when you are a Microsoft shop that wants the same model in Azure and on premises.
4. ABBYY FineReader Engine and FlexiCapture
ABBYY stays relevant in 2025 because of 3 things, accuracy on printed documents, very wide language coverage, and deep control over preprocessing and zoning. The current Engine and FlexiCapture products support 190 and more languages, export structured data, and can be embedded in Windows, Linux and VM workloads. ABBYY is also strong in regulated sectors where data cannot leave the premises.
Strengths
- Very high recognition quality on scanned contracts, passports, old documents.
- Largest language set in this comparison.
- FlexiCapture can be tuned to messy recurring documents.
- Mature SDKs.
Limits
- License cost is higher than open source.
- Deep learning based scene text is not the focus.
- Scaling to hundreds of nodes needs engineering.
Use when you must run on premises, must process many languages, or must pass compliance audits.
5. PaddleOCR 3.0
PaddleOCR 3.0 is an Apache licensed open source toolkit that aims to bridge images and PDFs to LLM ready structured data. It ships with PP OCRv5 for multilingual recognition, PP StructureV3 for document parsing and table reconstruction, and PP ChatOCRv4 for key information extraction. It supports 100 plus languages, runs on CPU and GPU, and has mobile and edge variants.
Strengths
- Free and open, no per page cost.
- Fast on GPU, usable on edge.
- Covers detection, recognition and structure in one project.
- Active community.
Limits
- You must deploy, monitor and update it.
- For European or financial layouts you often need postprocessing or fine tuning.
- Security and durability are your responsibility.
Use when you want full control, or you want to build a self hosted document intelligence service for LLM RAG.
6. DeepSeek OCR, Contexts Optical Compression
DeepSeek OCR was released in October 2025. It is not a classical OCR. It is an LLM centric vision language model that compresses long text and documents into high resolution images, then decodes them. The public model card and blog report around 97 percent decoding accuracy at 10 times compression and around 60 percent at 20 times compression. It is MIT licensed, built around a 3B decoder, and already supported in vLLM and Hugging Face. This makes it interesting for teams that want to reduce token cost before calling an LLM.
Strengths
- Self hosted, GPU ready.
- Excellent for long context and mixed text plus tables because compression happens before decoding.
- Open license.
- Fits modern agentic stacks.
Limits
- There is no standard public benchmark yet that puts it against Google or AWS, so enterprises must run their own tests.
- Requires a GPU with enough VRAM.
- Accuracy depends on chosen compression ratio.
Use when you want OCR that is optimized for LLM pipelines rather than for archive digitization.
Head to head comparison
| Feature | Google Cloud Document AI (Enterprise Document OCR) | Amazon Textract | Azure AI Document Intelligence | ABBYY FineReader Engine / FlexiCapture | PaddleOCR 3.0 | DeepSeek OCR |
|---|---|---|---|---|---|---|
| Core task | OCR for scanned and digital PDFs, returns text, layout, tables, KVP, selection marks | OCR for text, tables, forms, IDs, invoices, receipts, with sync and async APIs | OCR plus prebuilt and custom models, layout, containers for on premises | High accuracy OCR and document capture for large, multilingual, on premises workloads | Open source OCR and document parsing, PP OCRv5, PP StructureV3, PP ChatOCRv4 | LLM centric OCR that compresses document images and decodes them for long context AI |
| Text and layout | Blocks, paragraphs, lines, words, symbols, tables, key value pairs, selection marks | Text, relationships, tables, forms, query responses, lending analysis | Text, tables, KVP, selection marks, figure extraction, structured JSON, v4 layout model | Zoning, tables, form fields, classification through FlexiCapture | StructureV3 rebuilds tables and document hierarchy, KIE modules available | Reconstructs content after optical compression, good for long pages, needs local evaluation |
| Handwriting | Printed and handwriting for 50 languages | Handwriting in forms and free text | Handwriting supported in read and layout models | Printed very strong, handwriting available via capture templates | Supported, may need domain tuning | Depends on image and compression ratio, not yet benchmarked vs cloud |
| Languages | 200+ OCR languages, 50 handwriting languages | Main business languages, invoices, IDs, receipts | Major business languages, expanding in v4.x | 190–201 languages depending on edition, widest in this table | 100+ languages in v3.0 stack | Multilingual via VLM decoder, coverage good but not exhaustively published, test per project |
| Deployment | Fully managed Google Cloud | Fully managed AWS, synchronous and asynchronous jobs | Managed Azure service plus read and layout containers (2025) for on premises | On premises, VM, customer cloud, SDK centric | Self hosted, CPU, GPU, edge, mobile | Self hosted, GPU, vLLM ready, license to verify |
| Integration path | Exports structured JSON to Vertex AI, BigQuery, RAG pipelines | Native to S3, Lambda, Step Functions, AWS IDP | Azure AI Studio, Logic Apps, AKS, custom models, containers | BPM, RPA, ECM, IDP platforms | Python pipelines, open RAG stacks, custom document services | LLM and agent stacks that want to reduce tokens first, vLLM and HF supported |
| Cost model | Pay per 1,000 pages, volume discounts | Pay per page or document, AWS billing | Consumption based, container licensing for local runs | Commercial license, per server or per volume | Free, infra only | Free repo, GPU cost, license to confirm |
| Best fit | Mixed scanned and digital PDFs on Google Cloud, layout preserved | AWS ingestion of invoices, receipts, loan packages at scale | Microsoft shops that need custom models and hybrid | Regulated, multilingual, on premises processing | Self hosted document intelligence for LLM and RAG | Long document LLM pipelines that need optical compression |
What to use when
- Cloud IDP on invoices, receipts, medical forms: Amazon Textract or Azure Document Intelligence.
- Mixed scanned and digital PDFs for banks and telcos on Google Cloud: Google Document AI Enterprise Document OCR.
- Government archive or publisher with 150 plus languages and no cloud: ABBYY FineReader Engine and FlexiCapture.
- Startup or media company building its own RAG over PDFs: PaddleOCR 3.0.
- LLM platform that wants to shrink context before inference: DeepSeek OCR.
Google Document AI, Amazon Textract, and Azure AI Document Intelligence all deliver layout aware OCR with tables, key value pairs, and selection marks as structured JSON outputs, while ABBYY FineReader Engine 12 R7 and FlexiCapture export structured data in XML and the new JSON format and support 190 to 201 languages for on premises processing. PaddleOCR 3.0 provides Apache licensed PP OCRv5, PP StructureV3, and PP ChatOCRv4 for self hosted document parsing. DeepSeek OCR reports 97% decoding precision below 10x compression and about 60% at 20x, so enterprises must run local benchmarks before rollout in production workloads. Overall, OCR in 2025 is document intelligence first, recognition second.
References:
Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
