metadata

library_name: transformers
pipeline_tag: image-to-text
license: afl-3.0

Model Card for TrOCR_Math_handwritten

Model Details

TrOCR model fine-tuned on a part of the mathwriting dataset converted from InkML files into images. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.

Developed by: [More Information Needed]
Model type: Transformer OCR
License: afl-3.0
Finetuned from model [optional]: TrOCR_large_stage1

Uses

Here is how to use this model in PyTorch:

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests

url = "path/to/image"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
processor = TrOCRProcessor.from_pretrained('fhswf/TrOCR_Math_handwritten')
model = VisionEncoderDecoderModel.from_pretrained('fhswf/TrOCR_Math_handwritten')
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

Bias, Risks, and Limitations

You can use the raw model for optical character recognition (OCR) on images containing one mathematical formula.

Training Details

Training Data

This model was finetuned on a part of the mathwriting dataset converted from InkML files into images.

Evaluation

Percentage of correct recognition: 77.8%
Percentage of correct recognition with one error: 85.7%
Percentage of correct recognition with two error: 89.9%

BibTeX:

@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}
}