
# 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
{"language": ["en"], "tags": ["generated_from_trainer"], "metrics": ["cer"], "pipeline_tag": "image-to-text", "model-index": [{"name": "trocr-base-handwritten-OCR-handwriting_recognition_v2", "results": []}]}
transformers pytorch tensorboard vision-encoder-decoder generated_from_trainer image-to-text en endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #image-to-text #en #endpoints_compatible #has_space #region-us 

trocr-base-handwritten-OCR-handwriting\_recognition\_v2
=======================================================


This model is a fine-tuned version of 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: URL


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: URL


*Character Length for Training Dataset:*


!Input Character Length for Training Dataset/Handwriting%20Recognition/Images/Input%20Character%20Length%20Distribution%20for%20Training%URL)


*Character Length for Evaluation Dataset:*


!Input Character Length for Evaluation Dataset/Handwriting%20Recognition/Images/Input%20Characgter%20Length%20Distribution%20for%20Evaluation%URL)


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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #image-to-text #en #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #image-to-text #en #endpoints_compatible #has_space #region-us 
### 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### Framework versions


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