metadata

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

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: 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:

Character Length for Evaluation Dataset:

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