trocr-large-printed-cmc7_tesseract_MICR_ocr
| Property | Value |
|---|---|
| Parameter Count | 609M |
| Tensor Type | F32 |
| Base Model | microsoft/trocr-large-printed |
| Training Epochs | 5 |
What is trocr-large-printed-cmc7_tesseract_MICR_ocr?
This is a specialized fine-tuned version of Microsoft's TrOCR large printed model, specifically adapted for CMC7 and MICR text recognition tasks. The model leverages transformer architecture with vision-encoder-decoder capabilities, making it particularly effective for processing printed text in specialized formats like bank checks and financial documents.
Implementation Details
The model was trained using the Adam optimizer with carefully tuned hyperparameters (betas=0.9,0.999, epsilon=1e-08) and a linear learning rate scheduler. The training process utilized a batch size of 16 for both training and evaluation, with a learning rate of 5e-05.
- Transformer-based architecture with vision-encoder-decoder framework
- Fine-tuned using PyTorch 2.1.2 and Transformers 4.39.3
- Optimized for F32 tensor operations
Core Capabilities
- Specialized text recognition for CMC7 and MICR formats
- Enhanced printed text processing abilities
- Support for inference endpoints integration
- Compatible with TensorBoard for visualization
Frequently Asked Questions
Q: What makes this model unique?
This model specifically targets CMC7 and MICR text recognition, making it particularly valuable for financial document processing and bank check reading applications. Its large parameter count (609M) and specialized training make it highly capable for these specific use cases.
Q: What are the recommended use cases?
The model is best suited for applications involving printed text recognition in financial documents, particularly those containing CMC7 or MICR text formats. It can be effectively integrated into document processing pipelines that require high accuracy in reading standardized printed text.
