GOT-OCR-2.0-hf
| Property | Value |
|---|---|
| Author | stepfun-ai |
| Paper | arXiv:2409.01704 |
| Model Type | OCR Image-to-Text |
What is GOT-OCR-2.0-hf?
GOT-OCR-2.0-hf is a state-of-the-art OCR model that implements a unified end-to-end approach for text recognition across diverse document types. This Hugging Face implementation represents a significant advancement in OCR technology, capable of handling everything from plain documents to complex formats like mathematical formulas and sheet music.
Implementation Details
The model is implemented using the Hugging Face Transformers library and supports both CPU and GPU inference. It processes images up to 1024×1024 resolution and includes special features for handling multi-page documents and cropped patches.
- Supports batch processing of multiple images
- Handles formatted text output (LaTeX, markdown)
- Provides interactive OCR with region selection
- Enables multi-page processing without loops
Core Capabilities
- Plain document OCR
- Scene text recognition
- Formatted document processing
- Table and chart recognition
- Mathematical formula interpretation
- Sheet music recognition
- Region-specific text extraction
Frequently Asked Questions
Q: What makes this model unique?
The model's unified end-to-end approach allows it to handle multiple OCR tasks without requiring separate models or preprocessing steps. Its ability to process complex formats and maintain formatting information sets it apart from traditional OCR solutions.
Q: What are the recommended use cases?
The model is ideal for various applications including document digitization, academic paper processing, musical score digitization, and scientific document analysis. It's particularly useful when dealing with mixed content types or when formatting preservation is important.
