TrOCR Base Printed Model
| Property | Value |
|---|---|
| Parameter Count | 333M |
| Author | Microsoft |
| Paper | arXiv:2109.10282 |
| Downloads | 79,885 |
| Model Type | Vision-encoder-decoder |
What is trocr-base-printed?
TrOCR base printed is a transformer-based optical character recognition model specifically designed for processing printed text. Developed by Microsoft, this model represents a significant advancement in OCR technology by leveraging the power of transformer architectures for both image encoding and text generation.
Implementation Details
The model employs a sophisticated architecture combining two main components: a BEiT-based image transformer encoder and a RoBERTa-based text transformer decoder. Images are processed as 16x16 pixel patches with added positional embeddings, while the decoder generates text tokens autoregressively.
- Vision Transformer encoder initialized from BEiT
- Text Transformer decoder initialized from RoBERTa
- Supports F32 tensor operations
- Fine-tuned on the SROIE dataset
Core Capabilities
- High-accuracy OCR for printed text
- Single text-line image processing
- Efficient text extraction from documents
- Support for batch processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its use of transformer architecture in both encoding and decoding stages, making it particularly effective for printed text recognition. The combination of BEiT and RoBERTa pre-trained models provides robust performance for OCR tasks.
Q: What are the recommended use cases?
The model is specifically optimized for processing printed text in single-line images. It's ideal for document digitization, automated data extraction from printed materials, and general OCR applications where high accuracy is required.
