language:
- ko
tags:
- trocr
- image-to-text
license: mit
metrics:
- wer
- cer
widget:
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_2.jpg
example_title: 랜덤 문장 1
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_6.jpg
example_title: 랜덤 문장 2
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_3.jpg
example_title: 챗봇 1
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_5.jpg
example_title: 챗봇 2
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg
example_title: 뉴스 1
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_3.jpg
example_title: 뉴스 2
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_1.jpg
example_title: 영화 리뷰 1
- src: >-
https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_2.jpg
example_title: 영화 리뷰 2
TrOCR for Korean Language (PoC)
Overview
TrOCR has not yet released a multilingual model including Korean, so we trained a Korean 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.
Collecting data
Text data
We created training data by processing three types of datasets.
- News summarization dataset: https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko
- Naver Movie Sentiment Classification: https://github.com/e9t/nsmc
- Chatbot dataset: https://github.com/songys/Chatbot_data
For efficient data collection, each sentence was separated by a sentence separator library (Kiwi Python wrapper; https://github.com/bab2min/kiwipiepy), and as a result, 637,401 samples were collected.
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 klue/roberta-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 = 64
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("daekeun-ml/ko-trocr-base-nsmc-news-chatbot")
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot")
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=64)
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.