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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: trocr-base-handwritten-OCR-handwriting_recognition_v2 |
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results: [] |
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language: |
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- en |
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metrics: |
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- cer |
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pipeline_tag: image-to-text |
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--- |
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# trocr-base-handwritten-OCR-handwriting_recognition_v2 |
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This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2470 |
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- Cer: 0.0360 |
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## Model description |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/ssarkar445/handwriting-recognitionocr |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.4292 | 1.0 | 2500 | 0.4332 | 0.0679 | |
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| 0.2521 | 2.0 | 5000 | 0.2767 | 0.0483 | |
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| 0.1049 | 3.0 | 7500 | 0.2470 | 0.0360 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.12.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.12.1 |
