---
language:
- en
tags:
- generated_from_trainer
metrics:
- cer
pipeline_tag: image-to-text
base_model: microsoft/trocr-base-handwritten
model-index:
- name: trocr-base-handwritten-OCR-handwriting_recognition_v2
  results: []
---

# trocr-base-handwritten-OCR-handwriting_recognition_v2

This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten).
It achieves the following results on the evaluation set:
- Loss: 0.2470
- CER: 0.0360

## 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)/Handwriting%20Recognition/Handwriting%20Recognition_v2/Mini%20Handwriting%20OCR%20Project.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://www.kaggle.com/datasets/ssarkar445/handwriting-recognitionocr

_Character Length for Training Dataset:_

![Input Character Length for Training Dataset](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Images/Input%20Character%20Length%20Distribution%20for%20Training%20Dataset.png)

_Character Length for Evaluation Dataset:_

![Input Character Length for Evaluation Dataset](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Optical%20Character%20Recognition%20(OCR)/Handwriting%20Recognition/Images/Input%20Characgter%20Length%20Distribution%20for%20Evaluation%20Dataset.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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4292        | 1.0   | 2500 | 0.4332          | 0.0679 |
| 0.2521        | 2.0   | 5000 | 0.2767          | 0.0483 |
| 0.1049        | 3.0   | 7500 | 0.2470          | 0.0360 |

### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1