metadata

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
  - src: >-
      https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/2.jpg
    example_title: Example 4

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 introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository

This was possible thanks to daekun-ml and Niels Rogge 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:

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.

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

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/