How to choose an OCR Model

Part of AI Product Engineering

Hosted by Joe Barrow and Hamel Husain

Fri, Jul 10, 2026

5:00 PM UTC (45 minutes)

Virtual (Zoom)

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Hamel Husain and Shreya Shankar
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What you'll learn

When an OCR API is enough

Understand when AWS Textract, Datalab, or a similar API is the right choice because you want OCR without infrastructure

What self-hosted OCR actually requires

See the basic pieces of an OCR stack: model serving, document ingestion, batching, storage, observability, and output

What open models make possible

Learn where models like LightOnOCR, Chandra, and DotsOCR fit and improve outputs

How output format changes model selection

Compare document structure extraction, plain text, markdown, and layout-aware outputs

What you gain by owning the stack

See what you get from hosting having your own scale to 0 infrastructure (like with modal)

Why this topic matters

OCR can start as a simple API call, and that is often the right choice. But teams hit limits when they need better structure, custom outputs, lower cost, or more control. Joe Barrow will show when to use a managed OCR API, what a manageable OCR stack looks like, and what open models make possible.

You'll learn from

Joe Barrow

Senior Research Scientist at Adobe Research

Joe Barrow is a Senior Research Scientist at Adobe Research, working on training and efficiently serving VLMs for document tasks. He previously led the machine learning team at Pattern Data, building document processing pipelines for hundreds of millions of pages. His open source ML projects include commonforms (making PDF forms fillable), tinyhnsw (building a vector database for fun and no profit), and LambdaNet (Haskell deep learning framework).

Hamel Husain

ML Engineer with 20+ years of experience

Hamel Husain is a ML Engineer with 20+ years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI, for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools. Hamel is currently an independent consultant helping companies build AI products.

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