Image-to-Text
Transformers
Safetensors
German
vision-encoder-decoder
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  <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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  - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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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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  You can use the raw model for optical character recognition (OCR) on single text-line images of german handwriting.
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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  ## Training Details
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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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- [More Information Needed]
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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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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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  ## Evaluation
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  <!-- This section describes the evaluation protocols and provides the results. -->
 
 
 
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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  **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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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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  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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  <!-- 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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+ Levenshtein: 1.8
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+ WER (Word Error Rate): 17
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+ CER (Character Error Rate):
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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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+ ```