DeepSeek OCR Is Here—What You Need to Know

What Is DeepSeek OCR? A Quick Primer

Have you ever wondered how language models could keep an entire book in memory? That’s the challenge DeepSeek OCR aims to solve. Most large language models (LLMs) choke on long contexts because the number of tokens skyrockets, pushing computing costs through the roof. DeepSeek OCR flips this equation by transforming text into images, then back into text. In doing so, it compresses long documents by a factor of 7–20× while keeping accuracy above 90% in most cases. This isn’t just an incremental improvement—it’s a shift in how machines can remember.

Quick Summary

Question	Answer
What is DeepSeek OCR?	A vision–language model that encodes pages as images and decodes them back to text, compressing long documents 7–20× while maintaining high accuracy.
Why does it matter?	It solves the long‑context problem for LLMs—allowing models to handle millions of tokens by trading text tokens for a smaller number of visual tokens.
How does it work?	Through a two‑stage architecture: DeepEncoder compresses images into vision tokens, and a mixture‑of‑experts decoder reconstructs the original text.
Is it open source?	Yes. The model and weights are available on GitHub and Hugging Face.
Who benefits?	Developers building document understanding tools, medical record digitisation, financial and legal services, and anyone needing to scale context for LLMs.

Quick Digest

This article explores what DeepSeek OCR is, how it works, and why it might be the turning point that unlocks million‑token context windows. You’ll learn about its unique architecture (DeepEncoder + MoE decoder), the multi‑resolution design that adapts to different document complexities, and benchmark results showing 96 %+ precision at 10× compression. Real‑world case studies—from digitizing medical records to powering long‑context chatbots—demonstrate the technology’s practical impact. You’ll also get step‑by‑step instructions for running DeepSeek OCR yourself and discover how Clarifai’s compute infrastructure and local runners can accelerate experimentation and deployment.

Why Do We Need Context Optical Compression? 

The modern AI landscape is dominated by LLMs that excel at question answering, summarization, and creative writing. But long documents remain an Achilles’ heel. Traditional tokenizers break text into hundreds of thousands of tokens, and the cost of processing them scales quadratically with sequence length. Even recent models boasting 100 k or 1 M context windows struggle to capture the full breadth of long reports or books. As Andrej Karpathy observed, “maybe it makes more sense that all inputs to LLMs should only ever be images, even if you happen to have pure text input”.

The Cognitive Analogy

Think of how a person reads a dense report. They don’t memorize every word; they skim the page, capturing layout, headings, and key phrases. That mental snapshot becomes a compact representation, enabling efficient recall later. DeepSeek OCR mimics this cognitive trick: by rendering text as an image, it can represent hundreds of words with a fraction of the tokens. This context optical compression leverages the natural density of images: a single visual token can capture spacing, font, and structure that would otherwise require dozens of text tokens.

Beyond Tokenizers

Tokenizers have long been a pain point for AI developers. They must juggle byte encodings, Unicode characters, and often produce tokens that don’t match human intuition. Karpathy calls them “ugly, separate, not end-to-end”. DeepSeek’s vision-based approach eliminates tokenization entirely: every input is just an image. This allows LLMs to treat text, diagrams, and layout uniformly and paves the way for more flexible and secure processing. It also reduces the risk of jailbreaks arising from obscure byte sequences.

Expert Insights

• Haoran Wei and the DeepSeek research team emphasise that compressing long contexts via optical mapping isn’t just about efficiency—it could be the key to scaling agentic systems to millions of tokens.
  
  
• Jeffrey Emanuel, an AI researcher, notes that DeepSeek’s inversion of the token hierarchy “gets rid of the assumption that text tokens are the most efficient representation”—an insight that might reshape how we design future models.
  
  
• Clarifai’s solution engineers point out that context compression aligns perfectly with compute orchestration on Clarifai’s platform: compressing inputs reduces memory usage, enabling more parallel jobs and faster throughput on GPUs.
  
  

How Does DeepSeek OCR Work?

DeepSeek OCR is built on a deceptively simple idea: use vision encoders to compress text into a small set of visual tokens, then use a language model to reconstruct the original content. This section unpacks the architecture, training pipeline, and multi‑resolution design.

Two‑Stage Architecture

At the heart of DeepSeek OCR lie two components:

1. DeepEncoder – a 380 M‑parameter vision encoder that accepts an image of a document and produces a sequence of vision tokens. It combines SAM‑base (80 M params) for local perception and CLIP‑large (300 M params) for global context. Between them sits a 16× convolutional compressor that reduces the number of tokens by aggregating patches into higher-level representations.
   
   
2. DeepSeek‑3B‑MoE decoder – a Mixture‑of‑Experts (MoE) language model with 3 B parameters but only 570 M active parameters per token. It “expands” the vision tokens back into text, producing a high‑fidelity transcription of the original document.
   
   

An analogy: imagine photographing a dense page with a high‑resolution camera (DeepEncoder) and then using an expert calligrapher to recreate the entire page from that photograph (MoE decoder). The calligrapher focuses on small sections at a time, drawing on specialized experts for formulas, charts, and multiple languages.

Multi‑Resolution Modes and “Gundam” Design

Not all documents need the same level of detail. A simple invoice might compress well, while a legal contract with diagrams needs more tokens. DeepSeek OCR offers five modes:

• Tiny – 512×512 images with ~64 vision tokens. Perfect for quick scans and simple pages.
  
  
• Small – 640×640 images with ~100 tokens. A balanced default that works for most documents.
  
  
• Base – 1024×1024 images with ~256 tokens, capturing fine details.
  
  
• Large – 1280×1280 images with ~400 tokens for complex layouts.
  
  
• Gundam – a dynamic tiling mode combining multiple 640×640 tiles plus a 1024×1024 global view, producing n×100 + 256 tokens (n = 2–9). It’s ideal for ultra‑high‑resolution pages such as newspapers or engineering diagrams.
  
  

This flexibility enables developers to adjust the compression ratio. If you want near‑perfect accuracy, choose Large or Gundam. When speed and cost matter more, opt for Tiny or Small.

Training Pipeline and Data Engine

DeepSeek’s training regime is a story of scale and diversity:

• OCR 1.0 data (70 %) – 30 M pages of diverse PDFs across 100+ languages, including 2 M finely annotated pages for Chinese and English.
  
  
• OCR 2.0 data (Charts/Chemical/Geometry) – synthetic data like 10 M charts, 5 M chemical formulas and 1 M geometric diagrams. This ensures the model can handle formulas, graphs and diagrams.
  
  
• General vision data (20 %) – images from LAION and Wukong with labels for captioning, detection and grounding.
  
  
• Text‑only data (10 %) – 10 % of the corpus to ensure strong language skills.
  
  

Training occurs in two stages. First, DeepEncoder is trained with next‑token prediction on OCR data and general vision data. Then the full system is fine‑tuned to align vision and language components. The researchers used A100‑40 G GPUs and pipeline parallelism to handle the massive compute demand. The result is a model that can generate 200,000+ pages per day on a single GPU and scale to 33 million pages per day on a cluster.

Expert Insights

• Clarifai’s infrastructure team notes that DeepSeek’s heavy reliance on GPU memory makes it an ideal candidate for compute orchestration. By using Clarifai’s platform, you can spin up GPUs on demand and orchestrate large‑scale inference workloads efficiently.
  
  
• Vision transformer researchers highlight that the combination of windowed attention (SAM) and global attention (CLIP) is unique and allows DeepSeek to balance local detail with holistic understanding.
  
  
• Data scientists praise the data engine’s diversity, ensuring robust performance on handwritten notes, charts, tables, and multilingual text.

Benchmarking DeepSeek OCR: How Good Is It?

Metrics matter when you’re compressing text by an order of magnitude. DeepSeek’s developers evaluated the model on several benchmarks to understand this trade‑off.

Fox Benchmark – Precision vs. Compression

The Fox benchmark tests how well DeepSeek OCR retains text information at different compression ratios. Results show:

• >96 % accuracy at 10× compression when using 64–100 vision tokens.
  
  
• ~85–87 % accuracy at 15–20× compression.
  
  
• Table highlights: 64 tokens yield 96.5 % precision at 10× and 85.8 % at 15×; 100 tokens deliver 97.3 % precision at 10× and 87.1 % at 20×.
  
  

These numbers imply that for most practical tasks, using around 100 vision tokens gives near‑lossless compression. Pushing beyond 15× will save more tokens but will risk an accuracy drop.

OmniDocBench – Beating the State of the Art

On the OmniDocBench, a comprehensive document parsing benchmark, DeepSeek OCR achieves edit distance <0.25 while using fewer than 1,000 vision tokens. It outperforms leading OCR models that require more tokens. For instance, models like GOT‑OCR 2.0, Qwen 2.5‑VL and InternVL 3 need 1,500–6,000 tokens to reach similar accuracies. In other words, DeepSeek attains state‑of‑the‑art performance with 1/6 to 1/60 of the tokens.

Throughput and Scalability

One often‑overlooked metric is throughput—how many pages a system can process per unit of time. DeepSeek shines here:

• 200,000+ pages per day processed on a single A100‑40 G GPU.
  
  
• 33 million pages per day on a cluster of 20 nodes (160 A100 GPUs).
  
  
• This efficiency enables large organizations to generate training data at scale, which can feed improved LLMs and AI applications.

Expert Insights

• Jeffrey Emanuel describes DeepSeek’s Fox results as “jaw‑dropping,” highlighting that 97 % accuracy at 7× compression defies previous assumptions about vision tokens.
  
  
• Andrej Karpathy refers to DeepSeek as a potential “JPEG moment for AI”, hinting that this might open a new era of multimodal compression.
  
  
• Clarifai data scientists note that the throughput numbers align with compute orchestration goals: developers can pipeline thousands of documents through Clarifai’s platform while optimizing GPU utilization.

 

Real‑World Applications: Beyond Benchmarks

Benchmarks are impressive, but how does DeepSeek OCR perform in the real world? This section explores practical scenarios—some gleaned from case studies and others from our own experiments at Clarifai.

Digitising Archives and Books

Libraries and archives house millions of books and manuscripts. Traditional OCR often struggles with irregular fonts and complex layouts. With DeepSeek OCR, you can feed a scanned page into the model and retrieve structured Markdown or JSON. For example, scanning an 18th‑century mathematics treatise produced a Markdown file retaining headings, equations, and footnotes with less than 1/10 the token count of raw text. This accelerated downstream tasks like summarization and search, reducing indexing time from hours to minutes. Clarifai’s model inference service can handle these workloads at scale while automatically adjusting GPU allocation based on document complexity.

Medical Records Digitisation

Hospitals generate mountains of paperwork: physician notes, lab results, prescriptions. Manual data entry is slow and error‑prone. A case study by Sparkco found that implementing DeepSeek OCR in a hospital’s electronic health record (EHR) workflow led to a 50 % increase in data processing speed and a 35 % reduction in administrative costs. Record retrieval time dropped by 40 %, and data accuracy improved by 30 %. Clarifai’s compute orchestration tools further streamlined deployment by balancing loads across local and cloud runners, meeting HIPAA compliance requirements with secure encryption.

Legal and Financial Document Processing

Law firms and banks handle contracts, invoices, and regulatory filings. DeepSeek’s ability to preserve layout and parse complex tables makes it attractive for this domain. Imagine scanning a 100‑page contract and retrieving all tables, sections, and footnotes in structured format. A pilot at a financial services firm showed that automating due diligence with DeepSeek OCR reduced review time by 30 % while increasing completeness. Clarifai’s local runners ensure that sensitive documents remain on‑premises, satisfying stringent data privacy regulations.

Training Data Generation for LLMs

Training high‑quality LLMs requires massive datasets. DeepSeek’s throughput—over 200,000 pages/day per GPU—enables companies to quickly create and annotate corpora. Researchers can compress millions of pages into vision tokens, feed them through language models for pre‑training, and iterate faster. Clarifai customers use our model deployment features to schedule batch jobs, monitor resource usage, and scale automatically when demand spikes.

Long‑Context Chatbots and Agents

Modern chatbots often lose track of conversations after a few thousand tokens. With DeepSeek OCR, it’s possible to compress older chat rounds into images and feed them back into the model. This “visual memory” allows the agent to retain context over hundreds of thousands of tokens. Internal tests at Clarifai show improved continuity in legal research bots and patient‑care assistants. The compressed memory also frees up tokens for more nuanced reasoning, enabling richer interactions.

Expert Insights

• Hospital IT administrators praise DeepSeek’s ability to integrate with existing EHR systems and support API and framework integration like vLLM and Transformers.
  
  
• Compliance officers highlight the importance of data privacy; running DeepSeek via Clarifai’s local runner ensures that PHI (protected health information) never leaves a secure environment.
  
  
• RAG engineers see enormous potential for using DeepSeek as a preprocessing step before retrieval‑augmented generation: compress documents, store them in a vector database, and decompress them only when necessary.

Getting Started with DeepSeek OCR for Free

If you’re eager to experiment, the open‑source model is ready to download. Here’s how to set it up and integrate it with Clarifai’s tools.

Installing and Loading the Model

Install dependencies (on a Linux machine with CUDA support):

pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118

pip install flash-attn transformers==4.46.3 accelerate==1.1.1 safetensors==0.4.5 addict

Load the model using Hugging Face Transformers:

from transformers import AutoModel, AutoTokenizer

import torch

model_name = "deepseek-ai/DeepSeek-OCR"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model = AutoModel.from_pretrained(model_name, trust_remote_code=True, use_safetensors=True)

model = model.eval().cuda().to(torch.bfloat16)

Run inference on an image (PNG or JPG):

prompt = "<image>\n<|grounding|>Convert the document to markdown."

image_file = "/path/to/your/document.png"

output = model.infer(tokenizer, prompt=prompt, image_file=image_file, base_size=1024, image_size=640, crop_mode=True, save_results=True)

The output will be a Markdown or JSON representation of the document. You can adjust base_size and image_size parameters to control resolution and compression.

Clarifai Product Integration

Clarifai makes it easier to deploy and orchestrate DeepSeek OCR at scale:

• Compute Orchestration: Use Clarifai’s platform to manage GPU clusters. The platform automatically assigns workloads, scales across multiple GPUs, and optimizes memory usage. When running batch OCR jobs, set up a pipeline that pulls documents from your data source, submits them to the model, and stores results back into a database.
  
  
• Model Inference Services: Clarifai’s API provides endpoints for running inference on custom models. Upload DeepSeek OCR as a custom model, and call the API via REST or gRPC. The service abstracts away GPU provisioning so you can focus on building applications.
  
  
• Local Runners: For sensitive documents or offline environments, Clarifai’s local runner allows you to deploy DeepSeek OCR on‑premises. This ensures compliance with privacy regulations like HIPAA and GDPR while keeping latency low.
  
  

Tip: When selecting resolution modes, start with the Small mode for general documents. If you notice missing details (e.g., in tables or diagrams), switch to Base or Large. Clarifai’s monitoring tools help identify memory bottlenecks and suggest optimal settings.

Expert Insights

• Clarifai cloud engineers recommend using the base_size and image_size parameters to control memory consumption. A smaller image_size reduces tokens but may miss details; a larger one improves quality at the cost of memory.
  
  
• Developers highlight that batching multiple documents in parallel yields significant speedups when running on Clarifai’s platform—up to 20× faster than serial processing.
  
  
• Data privacy specialists remind you to enable encryption at rest and in transit when processing sensitive records, especially in healthcare and finance.
  
  

How Does DeepSeek OCR Compare to Other Models?

While DeepSeek’s results are impressive, it’s useful to place them alongside other OCR and vision‑language models to understand strengths and trade‑offs.

Traditional OCR Engines

Legacy systems like Tesseract rely on rule‑based pattern recognition. They are lightweight and run on CPUs, but they struggle with complex layouts, handwritten text, and multi‑language documents. Their accuracy drops dramatically in noisy or skewed images. DeepSeek surpasses them by combining deep learning with layout preservation.

Modern VLMs and OCR Models

GOT‑OCR 2.0, MinerU 2.0, Qwen 2.5‑VL, InternVL 3, and SmolDocling are state‑of‑the‑art OCR/VLM systems. However:

• They often consume 1,500–6,000 vision tokens per page to achieve similar accuracy.
  
  
• They may not support multi‑resolution modes or dynamic tiling, making them less flexible.
  
  
• Some models are trained primarily on synthetic data, lacking generalization to real-world pages.
  
  

DeepSeek’s advantages include:

• Fewer tokens (as low as 64–100) with high accuracy.
  
  
• Better layout retention thanks to the SAM + CLIP encoder design.
  
  
• Mixture‑of‑experts decoder enabling specialized processing of charts, formulas, and multilingual text.
  
  

Clarifai’s Recommendation: When the primary goal is high‑quality extraction from complex documents with minimal GPU cost, DeepSeek OCR stands out. For extremely large models that require full fidelity, consider using DeepSeek in Gundam or Base mode to ensure accuracy while still benefiting from token compression.

Expert Insights

• AI practitioners note that DeepSeek’s integration of SAM and CLIP outperforms models that rely solely on patch‑based vision transformers. The windowed attention handles local text, while global attention captures context.
  
  
• Engineers who tried SmolDocling point out that its compact size leads to lower accuracy; DeepSeek strikes a better balance between size and performance.
  
  
• Researchers emphasise that the mixture‑of‑experts approach keeps compute costs low by activating only relevant experts, unlike monolithic decoders.

Innovations and Future Directions

DeepSeek OCR does more than compress documents—it introduces a paradigm shift in how we think about memory, tokens and AI design.

A Path to Million‑Token Contexts

By compressing text into images, DeepSeek opens the door to 10 million‑token context windows. Instead of feeding an LLM a 500 k‑token transcript, you could render it as a series of images and process them with far fewer tokens. This has profound implications for research assistants that need to ingest entire knowledge bases or for digital memory systems that keep long conversation histories.

Memory Forgetting Mechanisms

The DeepSeek paper speculates on memory decay mechanisms: older context could be progressively downsampled into lower resolutions, consuming fewer tokens while retaining essential information. This mirrors human memory, where we forget details over time but remember the gist. Implementing such a feature in LLMs could reduce compute cost while preserving context for reasoning.

Eliminating Tokenizers

Vision‑text compression eliminates tokenizers, which historically cause fragmentation, inefficiency and vulnerability to attacks. By standardizing inputs as images, we sidestep encoding issues, making LLMs more robust. This might lead to hybrid models that natively accept audio, images, and text in a unified form.

Integration With Retrieval‑Augmented Generation

Combining DeepSeek OCR with retrieval systems could allow long‑term memory. You could compress documents, store them in a vector database as vision token embeddings, and retrieve relevant compressed snippets during queries. This approach would maximize context retention while minimizing compute.

Expert Insights

• Vision‑language researchers see DeepSeek as a stepping stone toward general‑purpose multimodal models. The ability to process charts, formulas and images seamlessly hints at future models where text and visuals are interchangeable.
  
  
• AI philosophers suggest that optical compression might mirror human cognition—where memory is stored as images rather than discrete tokens—which could lead to more humanlike AI systems
  
  
• Clarifai’s R&D team is exploring how to integrate DeepSeek into our RAG pipelines, compressing documents before indexing them for retrieval.

Implementation Considerations and Best Practices

While DeepSeek OCR is powerful, deploying it effectively requires thoughtful planning. Here are best practices gleaned from the paper, case studies, and our own experiments.

Hardware and Resolution Choices

DeepSeek runs best on modern GPUs. An A100‑40 G GPU can process around 200 k pages/day. For smaller tasks, consider using a Clarifai local runner on your own GPU cluster. When selecting resolution modes:

• Tiny or Small for simple documents like invoices.
  
  
• Base for detailed reports and academic papers.
  
  
• Large or Gundam when you need high fidelity or encounter complex layouts.
  
  

Monitor GPU memory usage with Clarifai’s platform. If you see memory errors, reduce the image_size or opt for a lower mode. Batch processing multiple documents can dramatically improve throughput.

Integration and Privacy

Use Clarifai’s API integration to automate document ingestion and retrieval. For sensitive data (e.g., medical or financial), deploy DeepSeek via local runners and ensure encryption. Comply with regulations like HIPAA and GDPR by controlling data flow and access logs.

Fine‑Tuning and Evaluation

Although DeepSeek performs well out of the box, domain‑specific fine‑tuning can improve accuracy. Use your own documents to create OCR 1.0–style datasets. Fine‑tune the model using Clarifai’s training infrastructure to align it with your domain vocabulary and formatting conventions. Evaluate performance by measuring edit distance and structural accuracy (e.g., table detection, chart parsing). Use Clarifai’s dataset and annotation tools to manage labelled data.

Expert Insights

• Compute operations engineers advise using mixed‑precision inference (bf16 or fp16) to save memory while maintaining accuracy—DeepSeek supports bf16 out of the box.
  
  
• Security auditors recommend regularly patching dependencies like PyTorch and Flash‑Attn to mitigate vulnerabilities.
  
  
• Cloud architects suggest combining DeepSeek with Clarifai’s autoscaling to handle spiky workloads without over‑provisioning resources.
  
  

Risks, Limitations and Ethical Considerations

No technology is perfect. Here are some caveats to consider when using DeepSeek OCR.

Over‑Compression and Accuracy Loss

While the model retains high accuracy at 10× compression, pushing beyond 20× can drop precision to ~60 %. For critical documents (e.g., legal or medical), err on the side of lower compression modes. Always validate the output against ground truth when accuracy matters.

Data Privacy and Security

Images can contain sensitive information—from social security numbers to medical diagnoses. Storing them in compressed form does not automatically anonymize data. Always encrypt images at rest and restrict access. Clarifai’s local runner helps enforce strict access controls, but organizational policies must ensure compliance with privacy laws like HIPAA (USA) and GDPR (EU).

Bias and Misrecognition

DeepSeek’s training data spans many languages and formats, but biases can still emerge. It may perform better on languages with more training samples (e.g., English and Chinese) than on low‑resource languages. Always test and, if necessary, fine‑tune the model for your specific language or domain.

Ethical Use

As with all AI, consider the broader impact of automating document processing. Ensure transparency when using AI outputs in legal or medical decisions. Avoid using the technology in ways that could harm individuals by extracting information without consent. Clarifai’s Responsible AI guidelines provide frameworks for ethical deployment.

Expert Insights

• Privacy advocates caution that converting text to images doesn’t remove confidentiality concerns. It may even create new attack surfaces if images are intercepted.
  
  
• Ethical AI researchers stress the need for human oversight when using compressed context for decision‑making, especially in high‑impact domains (healthcare, law, finance).
  
  
• Clarifai’s compliance team recommends conducting privacy impact assessments before deploying DeepSeek on sensitive datasets.

Conclusion: A Turning Point for Multimodal AI

DeepSeek OCR isn’t just another optical character recognition system—it’s a paradigm shift in how AI handles information. By rendering text as images, it compresses long documents into a fraction of the tokens, achieving >96 % accuracy at 10× compression and enabling million‑token context windows. Its two‑stage architecture (DeepEncoder + MoE decoder), multi‑resolution modes, and diverse training data make it adaptable to everything from medical records to legal contracts.

For developers and organizations, DeepSeek offers a practical way to scale LLM applications without exploding compute costs. When combined with Clarifai’s compute orchestration, model inference services and local runners, you can deploy this technology securely and efficiently, whether you’re digitizing archives, generating training data or building long‑context chatbots.

The future points toward even deeper integration of vision and language. Techniques like memory forgetting could help models manage enormous contexts, while tokenizer‑free processing removes long‑standing obstacles in NLP. DeepSeek OCR may very well be the JPEG moment for AI—a simple yet transformative innovation that changes how machines “see” and “remember” text. Now is the time to explore its potential and imagine what comes next.

FAQs

What is context optical compression?

It’s the process of converting text into images and then encoding those images into a small number of tokens. By doing so, models can process long documents with fewer tokens, reducing computational cost while maintaining accuracy.

How accurate is DeepSeek OCR?

On the Fox benchmark, the model achieves >96 % accuracy at 10× compression and ~85–87 % accuracy at 15–20× compression. It also beats state‑of‑the‑art models on the OmniDocBench with fewer tokens.

Does DeepSeek OCR support handwritten text and formulas?

Yes. The training data includes diverse samples such as handwritten notes, charts, chemical formulas and geometry figures, enabling the model to parse a wide variety of visual information.

Can I run DeepSeek OCR on my laptop?

The model is large (3 B parameters) and best suited for GPU inference. Running it on a CPU will be very slow. For local deployment, use a GPU (e.g., RTX 3090 or A100) or Clarifai’s local runner for on‑premises environments.

Is DeepSeek OCR available through Clarifai?

DeepSeek OCR isn’t a built‑in Clarifai model yet, but you can upload custom models and leverage Clarifai’s compute orchestration and inference services to deploy it. Our platform also offers tools for data annotation, model training and monitoring, making it easy to build complete OCR solutions.