GOT-OCR2_0
| Property | Value |
|---|---|
| Parameter Count | 716M |
| Model Type | Image-Text-to-Text Transformer |
| License | Apache-2.0 |
| Paper | General OCR Theory: Towards OCR-2.0 |
| Tensor Type | BF16 |
What is GOT-OCR2_0?
GOT-OCR2_0 is a state-of-the-art unified end-to-end OCR model that represents a significant advancement in optical character recognition technology. Developed by stepfun-ai, this model introduces a comprehensive approach to text recognition that goes beyond traditional OCR capabilities, supporting multiple languages and various text formatting scenarios.
Implementation Details
The model utilizes a transformer-based architecture with 716M parameters, implementing advanced vision-language processing techniques. It's optimized for BF16 precision and includes custom code for enhanced functionality. The implementation supports various OCR modes including plain text, formatted text, and fine-grained recognition with specific features for box detection and color analysis.
- Unified end-to-end architecture for comprehensive OCR tasks
- Multiple recognition modes: plain text, formatted text, and fine-grained OCR
- Support for multi-crop OCR and result rendering
- Integrated vision-language capabilities
Core Capabilities
- Plain text OCR with high accuracy
- Formatted text recognition with layout preservation
- Fine-grained OCR with box and color detection
- Multi-crop processing for complex documents
- HTML rendering of formatted OCR results
- Multilingual support
Frequently Asked Questions
Q: What makes this model unique?
GOT-OCR2_0 stands out for its unified approach to OCR, combining traditional text recognition with advanced formatting and layout understanding. Its ability to handle both plain and formatted text, along with fine-grained recognition features, makes it a versatile solution for various OCR applications.
Q: What are the recommended use cases?
The model is ideal for document digitization, automated form processing, multilingual text extraction, and scenarios requiring layout-aware text recognition. It's particularly useful for applications needing both basic OCR and advanced formatting preservation.
