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---
tags:
- generated_from_trainer
model-index:
- name: trocr-base-printed-synthetic_dataset_ocr
results:
- task:
type: image-to-text
name: Text Generation
dataset:
name: synthetic_dataset_ocr
type: synthetic_dataset_ocr
split: test
metrics:
- type: cer
value: 0.002896524170994806
name: CER
language:
- en
metrics:
- cer
pipeline_tag: image-to-text
---
# trocr-base-printed-synthetic_dataset_ocr
This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on an unknown dataset.
## Model description
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
## Intended uses & limitations
This model could be used to read labels with printed text.
## Training and evaluation data
Here is the link to the dataset that I used for this model: https://www.kaggle.com/datasets/ravi02516/20k-synthetic-ocr-dataset
_Character Length for Training Dataset:_
/20%2C000%20Synthetic%20Samples%20Dataset/Images/Input%20Characgter%20Length%20Distribution%20for%20Training%20Dataset.png)
_Character Length for Evaluation Dataset:_
/20%2C000%20Synthetic%20Samples%20Dataset/Images/Input%20Characgter%20Length%20Distribution%20for%20Evaluation%20Dataset.png)
## 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: 1
- mixed_precision_training: Native AMP
### Training results
CER = 0.003 (Actually, 0.002896524170994806)
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
*Note: Please make sure to give proper credit to the owner(s) of the data and developers of the model (microsoft/trocr-base-printed).
### Model Checkpoint
@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}}
### Metric (Character Error Rate [CER])
@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.} } |