---
base_model: microsoft/trocr-large-printed
tags:
- generated_from_trainer
model-index:
- name: trocr-large-printed-cmc7_tesseract_MICR_ocr
  results: []
license: bsd-3-clause
language:
- en
metrics:
- cer
pipeline_tag: image-to-text
---

# trocr-large-printed-cmc7_tesseract_MICR_ocr

This model is a fine-tuned version of [microsoft/trocr-large-printed](https://huggingface.co/microsoft/trocr-large-printed).

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/Tesseract%20MICR%20(CMC7%20Dataset)/TrOCR_cmc7_tesseractMICR.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril.

## Training and evaluation data

Dataset Source: https://github.com/DoubangoTelecom/tesseractMICR/tree/master/datasets/cmc7

**Histogram of Label Character Lengths**

![Histogram of Label Character Lengths](https://raw.githubusercontent.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/main/Optical%20Character%20Recognition%20(OCR)/Tesseract%20MICR%20(CMC7%20Dataset)/Images/Histogram%20of%20Label%20Character%20Length.png)


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

The Character Error Rate (CER) for this model is 0.004970720413999727.

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3