TrOCR Base Stage1 Model
| Property | Value |
|---|---|
| Parameter Count | 384M |
| Model Type | Vision-Encoder-Decoder |
| Tensor Type | F32 |
| Paper | View Paper |
| Downloads | 19,832 |
What is trocr-base-stage1?
TrOCR-base-stage1 is a transformer-based Optical Character Recognition (OCR) model developed by Microsoft. It combines an image transformer encoder with a text transformer decoder, utilizing pre-trained components from BEiT and RoBERTa respectively. This model represents a significant advancement in OCR technology, processing images as 16x16 fixed-size patches with linear embedding and position encoding.
Implementation Details
The model employs a sophisticated architecture where images are processed as sequences of patches. The encoder handles the visual aspects while the decoder generates text tokens autoregressively. The implementation uses PyTorch and integrates seamlessly with the Hugging Face transformers library.
- Pre-trained image encoder initialized from BEiT
- Text decoder initialized from RoBERTa weights
- 16x16 pixel patch processing
- Linear embedding with position encoding
- Autoregressive text generation
Core Capabilities
- Single text-line image OCR processing
- High-accuracy text extraction from images
- Support for RGB image inputs
- Batch processing capabilities
- Integration with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its transformer-based architecture that combines vision and text processing capabilities, leveraging pre-trained components from both domains. The model's ability to process images as patches and generate text autoregressively makes it particularly effective for OCR tasks.
Q: What are the recommended use cases?
The model is specifically designed for optical character recognition on single text-line images. It's ideal for applications requiring text extraction from images, document digitization, and automated text recognition systems.
