Optical Character Recognition (OCR) technology has seen remarkable advancement in recent years. While hosted solutions like Azure Computer Vision and Mistral OCR offer convenient APIs, many organizations need open-source alternatives. Whether for compliance with data privacy regulations or cost optimization at scale, you still need self-hosted OCR models for many use cases.

Takeaways

• Extracting from scanned documents: Tesseract
• Extracting from handwritten documents: TrOCR
• Extracting from structured documents: PaddleOCR
• Extracting from complex documents: Qwen2.5-VL
• Extracting from visual documents: Llama 3.2 Vision

Traditional ML vs. LLM-Based OCR

OCR models broadly fall into two categories:

1. Traditional ML Models: Purpose-built for text extraction, these models often use specialized computer vision architectures and post-processing pipelines.

2. LLM-Based Models: Newer multimodal large language models that can perform OCR as part of their general visual understanding capabilities.

You should generally start with more traditional OCR models, which are fast, cheap, and often very accurate, even for structured data like tables (you may need to fiddle around with some configuration options). For complex diagrams or other tricky cases, you may need to use an LLM-based OCR model, which will incur higher latency and cost.

Traditional ML-Based OCR Models

1. Tesseract OCR

Tesseract is the most widely-used open-source OCR engine.

• Key Features:
  • Supports 100+ languages
  • LSTM-based neural network architecture
  • Extensive documentation and community
  • Apache 2.0 license
• Best For: General document processing, especially printed text
• GPU Support: Limited, primarily CPU-based

2. EasyOCR

EasyOCR provides a Python-first approach to OCR.

• Key Features:
  • Simple Python API
  • 80+ supported languages
  • Built on PyTorch
  • Apache 2.0 license
  • Currently no support for handwritten text, but coming soon
• Best For: Quick integration in Python projects
• GPU Support: Native GPU acceleration

3. PaddleOCR

PaddleOCR is a lightweight OCR toolkit developed by PaddlePaddle.

• Key Features:
  • PP-OCRv4 with high accuracy for Chinese and English
  • Support for 80+ languages
  • Layout analysis and table recognition
  • Formula recognition capabilities
  • Apache 2.0 license
• Best For: Complex document processing, especially for Chinese text and structured documents
• GPU Support: Easiest with Docker image

4. docTR

docTR is a comprehensive document text recognition library developed by Mindee.

• Key Features:
  • Multiple text detection architectures (DBNet, LinkNet, FAST)
  • Various text recognition models (CRNN, SAR, MASTER, ViTSTR)
  • Support for both PyTorch and TensorFlow
  • Apache 2.0 license
• Best For: Flexible OCR pipeline with choice of architectures
• GPU Support: Docker image for GPU support

LLM-Based OCR Models

5. Microsoft TrOCR

TrOCR uses transformer architecture for OCR tasks.

• Key Features:
  • Transformer-based architecture
  • Strong handwriting recognition
  • Multiple language models available
  • MIT license
• Best For: Handwritten text recognition
• GPU Support: Full GPU acceleration

6. Donut

Donut is an OCR-free document understanding transformer developed by Clova AI.

• Key Features:
  • End-to-end document understanding without OCR
  • Strong performance on structured documents
  • Support for document classification and information extraction
  • MIT license
• Best For: Document understanding without traditional OCR pipeline
• GPU Support: Full GPU acceleration

7. Qwen2.5-VL

Qwen2.5-VL is a powerful multimodal model that excels at visual language tasks.

• Key Features:
  • Advanced visual language understanding
  • High accuracy on complex document layouts
  • Support for multiple languages
  • Apache 2.0 license
• Best For: Complex visual language understanding tasks
• GPU Support: Full GPU acceleration

8. Llama 3.2 Vision

Llama 3.2 Vision offers OCR as part of its multimodal capabilities.

• Key Features:
  • General visual understanding
  • Contextual text extraction
  • Multiple languages
  • Llama 2 Community License
• Best For: Combined visual-language tasks
• GPU Support: Requires GPU for inference

Running OCR Models at Scale

Most modern OCR models benefit significantly from GPU acceleration. While traditional models like Tesseract can run on CPU, newer transformer-based and LLM models require GPUs for practical inference speeds.

Modal provides serverless GPU infrastructure ideal for running OCR workloads at scale. With Modal, you can:

1. Deploy any open-source OCR model
2. Automatically scale based on demand
3. Pay only for actual processing time
4. Access the latest GPU hardware

Ready to start processing documents at scale? Try Modal or check out our documentation for more examples.

Additional Resources

• Modal’s Guide to GPU Computing
• Document OCR on Modal Example