readme: ---
  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.
  ```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 `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
  
  ```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("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.
  - https://github.com/daekeun-ml/sm-kornlp-usecases/tree/main/trocr