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---
license: mit
language:
- fr
pipeline_tag: image-to-text
tags:
- trocr
- vision-encoder-decoder
metrics:
- cer
- wer
widget:
- src: >-
https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/3.jpg
example_title: Example 1
- src: >-
https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/0.jpg
example_title: Example 2
- src: >-
https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/1.jpg
example_title: Example 3
---
# TrOCR for French
## Overview
TrOCR has not yet released for French, so we trained a French model for PoC purpose. Based on this model, it is recommended to collect more data to additionally train the 1st stage or perform fine-tuning as the 2nd stage.
It's a special case of the [English trOCR model](https://huggingface.co/microsoft/trocr-base-printed) 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)
This was possible thanks to [daekun-ml](https://huggingface.co/daekeun-ml/ko-trocr-base-nsmc-news-chatbot) and [Niels Rogge](https://github.com/NielsRogge/) than enabled us to publish this model with their tutorials and code.
## Collecting data
### Text data
We created training data of ~723k examples by taking random samples of the following datasets:
- [MultiLegalPile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile) - 90k
- [French book Reviews](https://huggingface.co/datasets/Abirate/french_book_reviews) - 20k
- [WikiNeural](https://huggingface.co/datasets/Babelscape/wikineural) - 83k
- [Multilingual cc news](https://huggingface.co/datasets/intfloat/multilingual_cc_news) - 119k
- [Reviews Amazon Multi](https://huggingface.co/datasets/amazon_reviews_multi) - 153k
- [Opus Book](https://huggingface.co/datasets/opus_books) - 70k
- [BerlinText](https://huggingface.co/datasets/biglam/berlin_state_library_ocr) - 38k
We collected parts of each of the datasets and then cut randomly the sentences to collect the final training set.
### Image Data
Image data was generated with TextRecognitionDataGenerator (https://github.com/Belval/TextRecognitionDataGenerator) introduced in the TrOCR paper.
Below is a code snippet for generating images.
```shell
python3 ./trdg/run.py -i ocr_dataset_poc.txt -w 5 -t {num_cores} -f 64 -l ko -c {num_samples} -na 2 --output_dir {dataset_dir}
```
## Training
### Base model
The encoder model used `facebook/deit-base-distilled-patch16-384` and the decoder model used `camembert-base`. It is easier than training by starting weights from `microsoft/trocr-base-stage1`.
### Parameters
We used heuristic parameters without separate hyperparameter tuning.
- learning_rate = 4e-5
- epochs = 25
- fp16 = True
- max_length = 32
### Results on dev set
For the dev set we got those results
- size of the test set: 72k examples
- CER: 0.13
- WER: 0.26
- Val Loss: 0.424
## Usage
```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer
import requests
from io import BytesIO
from PIL import Image
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained("agomberto/trocr-base-printed-fr")
tokenizer = AutoTokenizer.from_pretrained("agomberto/trocr-base-printed-fr")
url = "https://github.com/agombert/trocr-base-printed-fr/blob/main/sample_imgs/0.jpg"
response = requests.get(url)
img = Image.open(BytesIO(response.content))
pixel_values = processor(img, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values, max_length=32)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
```
All the code required for data collection and model training has been published on the author's Github.
- https://github.com/agombert/trocr-base-printed-fr/ |