TROCR-LARGE-PRINTED-CMC7_TESSERACT_MICR_OCR

Introduction

The TROCR-LARGE-PRINTED-CMC7_TESSERACT_MICR_OCR model is a fine-tuned version of Microsoft's trocr-large-printed model. It is designed for optical character recognition (OCR) tasks. The model has been fine-tuned on an unspecified dataset and can be used for transforming printed text images into text.

Architecture

The model is based on the vision-encoder-decoder architecture. Its specific configuration and the architecture details have not been provided.

Training

Training Procedure

The model was fine-tuned using the following hyperparameters:

• Learning Rate: 5e-05
• Training Batch Size: 16
• Evaluation Batch Size: 16
• Seed: 42
• Optimizer: Adam (betas=(0.9, 0.999), epsilon=1e-08)
• Learning Rate Scheduler Type: Linear
• Number of Epochs: 5

Framework Versions

• Transformers: 4.39.3
• Pytorch: 2.1.2
• Datasets: 2.18.0
• Tokenizers: 0.15.2

Guide: Running Locally

To run the model locally, follow these steps:

1. Clone the repository and install the dependencies listed in the requirements.txt file.
2. Ensure you have Python and the necessary libraries installed, including Pytorch and Transformers.
3. Load the model using the Transformers library.

For optimal performance, it is recommended to use a cloud GPU service such as AWS EC2 with GPU instances, Google Cloud Platform, or Azure.

License

The licensing information for this model is not provided. Please check the original model page for more details.