Mangaocr Hoogberta V2

🚀 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

# 你可以根據實際需求，對預測函數進行擴展，例如批量處理圖片等。
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：

• 測試圖片1
• 測試圖片2
• 測試圖片3
• 測試圖片4
• 測試圖片5
• 測試圖片6