🚀 漫畫OCR模型(MangaOCR)
本項目是一個圖像轉文字的模型,利用Trocr技術,能夠將漫畫圖像中的文字準確識別出來,為漫畫文字處理提供了高效的解決方案。
🚀 快速開始
以下是在PyTorch中使用該模型的示例代碼:
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
processor = TrOCRProcessor.from_pretrained('dsupa/mangaocr-hoogberta-v2')
model = VisionEncoderDecoderModel.from_pretrained('dsupa/mangaocr-hoogberta-v2')
def predict(image_path):
image = Image.open(image_path).convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
image_path = "your_img.jpg"
pred = predict(image_path)
print(pred)
💻 使用示例
基礎用法
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
processor = TrOCRProcessor.from_pretrained('dsupa/mangaocr-hoogberta-v2')
model = VisionEncoderDecoderModel.from_pretrained('dsupa/mangaocr-hoogberta-v2')
def predict(image_path):
image = Image.open(image_path).convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
image_path = "your_img.jpg"
pred = predict(image_path)
print(pred)
高級用法
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import os
processor = TrOCRProcessor.from_pretrained('dsupa/mangaocr-hoogberta-v2')
model = VisionEncoderDecoderModel.from_pretrained('dsupa/mangaocr-hoogberta-v2')
def predict(image_path):
image = Image.open(image_path).convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
image_folder = "your_image_folder"
for image_name in os.listdir(image_folder):
image_path = os.path.join(image_folder, image_name)
pred = predict(image_path)
print(f"Image: {image_name}, Prediction: {pred}")
測試示例
你可以使用以下圖片鏈接進行模型測試: