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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-printed-synthetic_dataset_ocr |
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results: |
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- task: |
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type: image-to-text |
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name: Text Generation |
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dataset: |
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name: synthetic_dataset_ocr |
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type: synthetic_dataset_ocr |
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split: test |
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metrics: |
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- type: cer |
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value: 0.002896524170994806 |
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name: CER |
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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-printed-synthetic_dataset_ocr |
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This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on an unknown dataset. |
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## Model description |
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Here is the link to my code for this model: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/tree/main/Optical%20Character%20Recognition%20(OCR)/20%2C000%20Synthetic%20Samples%20Dataset |
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## Intended uses & limitations |
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This model could be used to read labels with printed text. |
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## Training and evaluation data |
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Here is the link to the dataset that I used for this model: https://www.kaggle.com/datasets/ravi02516/20k-synthetic-ocr-dataset |
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_Character Length for Training Dataset:_ |
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/20%2C000%20Synthetic%20Samples%20Dataset/Images/Input%20Characgter%20Length%20Distribution%20for%20Training%20Dataset.png) |
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_Character Length for Evaluation Dataset:_ |
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/20%2C000%20Synthetic%20Samples%20Dataset/Images/Input%20Characgter%20Length%20Distribution%20for%20Evaluation%20Dataset.png) |
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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: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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CER = 0.003 (Actually, 0.002896524170994806) |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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*Note: Please make sure to give proper credit to the owner(s) of the data and developers of the model (microsoft/trocr-base-printed). |
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### Model Checkpoint |
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@misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL}} |
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### Metric (Character Error Rate [CER]) |
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@inproceedings{morris2004, author = {Morris, Andrew and Maier, Viktoria and Green, Phil}, year = {2004}, month = {01}, pages = {}, title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.} } |
