TrOCR Small Printed
| Property | Value |
|---|---|
| Parameter Count | 61.4M |
| Model Type | Vision Encoder-Decoder |
| Paper | TrOCR: Transformer-based OCR with Pre-trained Models |
| Author | Microsoft |
| Downloads | 144,948 |
What is trocr-small-printed?
TrOCR small-printed is a specialized optical character recognition (OCR) model designed specifically for processing printed text. Developed by Microsoft, this compact model combines the power of transformer architecture with efficient design, making it particularly suitable for production environments where resource optimization is crucial.
Implementation Details
The model employs a sophisticated dual-transformer architecture: an image transformer encoder initialized from DeiT weights, and a text transformer decoder initialized from UniLM. Images are processed as 16x16 pixel patches with added position embeddings, enabling efficient text recognition from image inputs.
- Transformer-based vision encoder for image processing
- Autoregressive text decoder for sequential text generation
- Fine-tuned on SROIE dataset for optimal performance
- Supports PyTorch framework with Hugging Face integration
Core Capabilities
- Single text-line image processing
- Printed text recognition with high accuracy
- Efficient processing with 61.4M parameters
- Integration with common ML pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that balances performance with model size, making it particularly suitable for production deployments where resource constraints exist. The combination of DeiT-based image encoding and UniLM-based text decoding creates a powerful yet manageable OCR solution.
Q: What are the recommended use cases?
The model is specifically optimized for processing printed text in single-line images. It's ideal for applications like document digitization, receipt processing, and automated data extraction from printed materials. The model has been fine-tuned on the SROIE dataset, making it particularly effective for processing structured documents.
