đ trocr-base-printed_captcha_ocr
This model is a fine - tuned version of microsoft/trocr-base-printed, which can extract text from image Captcha inputs. It achieves certain results on the evaluation set, demonstrating the effectiveness of the fine - tuning process.
đ Quick Start
This part provides a high - level overview of the model. The model is a fine - tuned version of microsoft/trocr-base-printed on an unknown dataset. It has achieved the following results on the evaluation set:
⨠Features
- Text Extraction: This model can extract text from image Captcha inputs.
- Fine - Tuned: It is a fine - tuned version of microsoft/trocr-base-printed, potentially having better performance on specific tasks.
đ Documentation
Model description
This model extracts text from image Captcha inputs. 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)/Captcha/OCR_captcha.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/alizahidraja/captcha-data
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 |
| 10.4464 |
1.0 |
107 |
0.5615 |
0.0879 |
| 10.4464 |
2.0 |
214 |
0.2432 |
0.0262 |
| 10.4464 |
3.0 |
321 |
0.1380 |
0.0075 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
đ License
No license information is provided in the original document, so this section is skipped.
đ§ Technical Details
The model is a fine - tuned version of microsoft/trocr-base-printed. The training process uses specific hyperparameters and the Adam optimizer. The learning rate scheduler is of the linear type, and the model is trained for 3 epochs. The training and validation losses and CER values are recorded during the training process, which shows the model's performance improvement over epochs.
đĻ Installation
No installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
| Property |
Details |
| Model Type |
Fine - tuned version of microsoft/trocr-base-printed |
| Training Data |
Dataset Source: https://www.kaggle.com/datasets/alizahidraja/captcha-data |
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