Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 license: mit
+datasets:
+- agomberto/FrenchCensus-handwritten-texts
+language:
+- fr
+pipeline_tag: image-to-text
+tags:
+- pytorch
+- transformers
+- trocr
+widget:
+- src: >-
+    https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/4.png
+  example_title: Example 1
+- src: >-
+    https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/5.jpg
+  example_title: Example 2
+metrics:
+- cer
+- wer
 ---
+
+# TrOCR base handwritten for French
+
+## Overview
+
+TrOCR handwritten 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 large handwritten trOCR model](https://huggingface.co/microsoft/trocr-large-handwritten) 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) as a TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database).
+
+We decided to fine-tuned on two datasets:
+1. [French Census dataset](https://zenodo.org/record/6581158) from Constum et al. We created a [dataset on the hub](https://huggingface.co/datasets/agomberto/FrenchCensus-handwritten-texts) too.
+2. A dataset soon to come on French archives
+
+## Model description
+
+The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa.
+
+Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens.
+
+## Intended uses & limitations
+
+You can use the raw model for optical character recognition (OCR) on single text-line images.
+
+## Parameters
+We used heuristic parameters without separate hyperparameter tuning.
+- learning_rate = 4e-5
+- epochs = 10
+- fp16 = True
+- max_length = 32
+
+## Results on dev set
+
+For the dev set we got those results
+- size of the test set: 1300 examples
+- CER: 0.12
+- WER: 0.37
+
+For the test set (from French Census only) we got those results
+- size of the test set: 700 examples
+- CER: 0.16
+- WER: 0.81
+
+### How to use
+
+Here is how to use this model in PyTorch:
+
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer
+from PIL import Image
+import requests
+
+url = "https://github.com/agombert/trocr-base-printed-fr/blob/main/sample_imgs/5.jpg"
+response = requests.get(url)
+img = Image.open(BytesIO(response.content))
+
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-handwritten')
+model = VisionEncoderDecoderModel.from_pretrained('agomberto/trocr-large-handwritten-fr')
+tokenizer = AutoTokenizer.from_pretrained('agomberto/trocr-large-handwritten-fr')
+
+pixel_values = (processor(images=image, return_tensors="pt").pixel_values)
+generated_ids = model.generate(pixel_values)
+generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```