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