Create README.md

README.md

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+---
+language: 
+  - ko
+tags:
+- trocr
+- image-to-text
+license: mit
+metrics:
+- wer
+- cer
+---
+
+# 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 summariation 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
+We used heuristic parameters without separate hyperparameter tuning.
+- learning_rate = 4e-5
+- epochs = 25
+- fp16 = True
+
+## 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/aws-ai-ml-workshop-kr/master/sagemaker/sm-kornlp/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