Update README.md

README.md

@@ -1,3 +1,103 @@
 ---
 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/0.jpg
+    example_title: Example 1
+  - src: >-
+      https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/1.jpg
+    example_title: Example 2
+  - src: >-
+      https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/2.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
+
+### inference.py
+
+```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://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.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/