GOT-OCR2.0 in Action: Optical Character Recognition Applications and Code Examples

I’ve been diving into GOT-OCR2.0 lately, and it’s pretty impressive.

I thought I’d walk you through some code examples and share what I’ve learned so far, since it could be a key component for some of your projects.

GOT-OCR2.0 stands for General OCR Theory 2.0, and it’s a fresh take on optical character recognition.

Traditional OCR systems (what they call OCR-1.0) usually involve complex pipelines with multiple modules — think element detection, region cropping, character recognition, and so on.

Each of these modules can be a pain to maintain and optimize.

GOT-OCR2.0 simplifies this by introducing an end-to-end architecture. It’s built on an encoder-decoder paradigm:

• Encoder: A high compression rate encoder that can handle high-resolution images (up to 1024×1024 pixels). It compresses the image into a manageable number of tokens (256×1024 dimensions).
• Decoder: A decoder with a long context length (supports up to 8K tokens), allowing it to handle lengthy and dense text outputs.

What’s really awesome about GOT-OCR2.0 is how it streamlines everything into a single model.