RolmOCR
| Property | Value |
|---|---|
| Base Model | Qwen2.5-VL-7B |
| License | Apache 2.0 |
| Model URL | https://huggingface.co/reducto/RolmOCR |
| Author | Reducto AI |
What is RolmOCR?
RolmOCR is an innovative OCR solution developed by Reducto AI as a drop-in replacement for olmOCR. Built on the Qwen2.5-VL-7B vision language model, it offers enhanced performance with reduced memory requirements. This model represents a significant advancement in document OCR technology, designed to efficiently process PDFs and complex documents while maintaining high accuracy.
Implementation Details
The model implements several key optimizations over its predecessor, including the adoption of the newer Qwen2.5-VL-7B foundation model and the removal of metadata dependency. Training involved rotating 15% of the data to improve handling of off-angle documents, while maintaining the original training dataset structure.
- Streamlined architecture without metadata inputs for reduced VRAM usage
- Enhanced document rotation handling through augmented training data
- Optimized for faster processing while maintaining accuracy
- Built on the advanced Qwen2.5-VL-7B architecture
Core Capabilities
- Efficient document text extraction
- Reduced memory footprint compared to olmOCR
- Support for various document types
- Natural text representation output
- Easy deployment with vLLM support
Frequently Asked Questions
Q: What makes this model unique?
RolmOCR stands out for its optimized performance and reduced resource requirements while maintaining high accuracy. The removal of metadata dependencies and implementation of document rotation handling make it particularly efficient for real-world applications.
Q: What are the recommended use cases?
The model is ideal for organizations needing to process large volumes of documents with varied layouts and orientations. It's particularly suitable for applications where memory efficiency and processing speed are crucial, without compromising on OCR accuracy.
