Trocr Small Photomath

🚀 TrOCR (Small-sized Model, Fine-tuned on Synthetic Math Expression Dataset)

This is a TrOCR model fine-tuned on the Synthetic Math Expression Dataset, which can be used for optical character recognition tasks.

🚀 Quick Start

The TrOCR model fine-tuned on the Synthetic Math Expression Dataset is introduced here. It was first presented in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and initially released in this repository.

Disclaimer: The team releasing TrOCR did not write a model card for this model, so this model card has been written by the Hugging Face team.

✨ Features

The TrOCR model is an encoder - decoder model, composed of an image Transformer as the encoder and a text Transformer as the decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa.

Images are presented to the model as a sequence of fixed - size patches (resolution 16x16), which are linearly embedded. Absolute position embeddings are added before feeding the sequence to the layers of the Transformer encoder. Then, the Transformer text decoder autoregressively generates tokens.

📚 Documentation

Intended uses & limitations

You can use the raw model for optical character recognition (OCR) on single text - line images. Check the model hub to find fine - tuned versions for tasks that interest you.

💻 Usage Examples

Basic Usage

Here is how to use this model in PyTorch:

from transformers import VisionEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer
from PIL import Image
import requests

# load image from the IAM database
url = 'https://drive.google.com/uc?export=view&id=15dUjO44YDe1Agw_Qi8MyODRHpUFaCFw-'
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

feature_extractor = AutoFeatureExtractor.from_pretrained('vukpetar/trocr-small-photomath')
tokenizer = AutoTokenizer.from_pretrained("vukpetar/trocr-small-photomath")
model = VisionEncoderDecoderModel.from_pretrained('vukpetar/trocr-small-photomath')
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values

generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

📄 License

BibTeX entry and citation info

@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}
}