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--- |
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library_name: transformers |
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datasets: |
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- fhswf/german_handwriting |
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language: |
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- de |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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<!-- Provide a longer summary of what this model is. --> |
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TrOCR model fine-tuned on the [german_handwriting](https://huggingface.co/datasets/fhswf/german_handwriting). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). |
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- **Developed by:** [More Information Needed] |
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- **Model type:** Transformer OCR |
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- **Language(s) (NLP):** German |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [TrOCR_large_handwritten](https://huggingface.co/microsoft/trocr-large-handwritten) |
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## Uses |
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Here is how to use this model in PyTorch: |
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```python |
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from transformers import TrOCRProcessor, VisionEncoderDecoderModel |
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from PIL import Image |
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import requests |
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# load image from the IAM database |
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url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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processor = TrOCRProcessor.from_pretrained('TGrote11/testModel') |
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model = VisionEncoderDecoderModel.from_pretrained('TGrote11/testModel') |
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pixel_values = processor(images=image, return_tensors="pt").pixel_values |
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generated_ids = model.generate(pixel_values) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## Bias, Risks, and Limitations |
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You can use the raw model for optical character recognition (OCR) on single text-line images of german handwriting. |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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This model was finetuned on [german_handwriting](https://huggingface.co/datasets/fhswf/german_handwriting). |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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Levenshtein: 1.85 \\ |
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WER (Word Error Rate): 17.5% \\ |
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CER (Character Error Rate): 4.1% |
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**BibTeX:** |
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```bibtex |
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@misc{li2021trocr, |
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title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, |
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author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, |
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year={2021}, |
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eprint={2109.10282}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
