In this episode, we dive deep into DeepSeek-OCR, a cutting-edge open-source Optical Character Recognition (OCR) / Text Recognition model that's redefining accuracy and efficiency in document understanding.

DeepSeek-OCR flips long-context processing on its head by rendering text as images and then decoding it back—shrinking context length by 7–20× while preserving high fidelity.

We break down how the two-stage stack works—DeepEncoder (optical/vision encoding of pages) + MoE decoder (text reconstruction and reasoning)—and why this "context optical compression" matters for million-token workflows, from legal PDFs to scientific tables.

We also dive into accuracy trade-offs (≈96–97% at ~10× compression), benchmarks, and practical implications for cost, latency, and multimodal RAG. If you care about scaling LLMs beyond brittle token limits, this is the paradigm shift to watch.

Resources:

1. DeepSeek-OCR Repo: https://github.com/deepseek-ai/DeepSeek-OCR/tree/main
2. DeepSeek-OCR Paper: https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf
3. Start your AI career: https://opencv.org/university
4. Need help in building AI solutions? https://bigvision.ai