Trocr Base Printed Synthetic Dataset Ocr

🚀 trocr-base-printed-synthetic_dataset_ocr

This model is a fine - tuned version of microsoft/trocr-base-printed on an unknown dataset, which can be used for image - to - text tasks.

🚀 Quick Start

This model is a fine - tuned version of microsoft/trocr-base-printed on an unknown dataset.

✨ Features

This model could be used to read labels with printed text.

📚 Documentation

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:

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: 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

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.} }

⚠️ Important Note

Please make sure to give proper credit to the owner(s) of the data and developers of the model (microsoft/trocr-base-printed).

📄 License

No license information is provided in the original document, so this section is skipped.

🔧 Technical Details

No specific technical details beyond the training hyperparameters and framework versions are provided. Since the content is relatively brief and does not meet the requirement of more than 50 - word specific technical description, this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.