TeamUNIVA
/

23MATHQ_TrOCR-large

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 library_name: transformers
-tags: []
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 # Model Card for Model ID
@@ -10,28 +11,8 @@ tags: []
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@@ -39,161 +20,43 @@ This is the model card of a 🤗 transformers model that has been pushed on the
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 ---
 library_name: transformers
+language:
+- ko
 ---
 # Model Card for Model ID
 ## Model Details
+line 단위로 수식이 포함된 글자를 인식 모델입니다.
+한국어 + latex 데이터셋으로 finetuning 했습니다.
 ## Uses
 ### Direct Use
+```python
+from PIL import Image
+import glob
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import torch
+import IPython.display as ipd
+## 이미지 준비
+img_path_list = sorted(glob.glob('images/mathematical_expression_2-*.png'))
+img_list = [Image.open(img_path).convert("RGB") for img_path in img_path_list]
+## 모델 및 프로세서 준비
+model_path = 'models/math_ocr'
+processor = TrOCRProcessor.from_pretrained(model_path)
+model = VisionEncoderDecoderModel.from_pretrained(model_path)
+model.eval()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+processor.feature_extractor.size = model.config.encoder.image_size
+gc = model.generation_config
+gc.max_length = 128
+gc.early_stopping = True
+gc.no_repeat_ngram_size = 3
+gc.length_penalty = 2.0
+gc.num_beams = 4
+gc.eos_token_id = processor.tokenizer.sep_token_id
+## TrOCR 추론
+pixel_values = processor(img_list, return_tensors="pt").pixel_values
+generated_ids = model.generate(pixel_values.to(model.device), pad_token_id=processor.tokenizer.eos_token_id)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+for img,text in zip(img_list, generated_text):
+    ipd.display(img)
+    print(text)
+```