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这页内容比较潦草，帮我识别这个图片里的所有文字

Criado em: 8 de janeiro de 2026

Respondido usando O3 por Chat01

Pergunta

09b816fe5c184d8a27fb25890fead355.jpg

这页内容比较潦草，帮我识别这个图片里的所有文字

Python

python
from PIL import Image
import matplotlib.pyplot as plt

img = Image.open('/mnt/data/09b816fe5c184d8a27fb25890fead355.jpg')
plt.imshow(img)
plt.axis('off')

(-0.5, 1079.5, 1919.5, -0.5)

Python

python
plt.figure(figsize=(6,10))
plt.imshow(img.rotate(90, expand=True))
plt.axis('off')

(-0.5, 1919.5, 1079.5, -0.5)

Python

python
plt.figure(figsize=(6,10))
plt.imshow(img.rotate(-90, expand=True))
plt.axis('off')

(-0.5, 1919.5, 1079.5, -0.5)

Python

python
rot = img.rotate(90, expand=True)
plt.imshow(rot)
plt.axis('off')

(-0.5, 1919.5, 1079.5, -0.5)

Python

python
rot2 = img.rotate(-90, expand=True)
plt.imshow(rot2)
plt.axis('off')

(-0.5, 1919.5, 1079.5, -0.5)

Python

python
plt.figure(figsize=(10,4))
plt.imshow(rot.crop((0,0,1200,500)))
plt.axis('off')

(-0.5, 1199.5, 499.5, -0.5)

Python

python
plt.figure(figsize=(8,4))
plt.imshow(rot.crop((400,180,1100,380)))
plt.axis('off')

(-0.5, 699.5, 199.5, -0.5)

Python

python
plt.figure(figsize=(10,6))
plt.imshow(rot)
plt.show()

Python

python
plt.figure(figsize=(10,4))
plt.imshow(rot.crop((600,100,1400,250)))
plt.axis('off')

(-0.5, 799.5, 149.5, -0.5)

Python

python
plt.figure(figsize=(10,4))
plt.imshow(rot.crop((450,100,1050,250)))
plt.axis('off')

(-0.5, 599.5, 149.5, -0.5)

Python

python
plt.figure(figsize=(10,4))
# crop bigger area
plt.imshow(rot.crop((400,50,1600,400)))
plt.axis('off')

(-0.5, 1199.5, 349.5, -0.5)

Python

python
plt.figure(figsize=(6,4))
plt.imshow(rot.crop((500,170,1000,320)))
plt.axis('off')

(-0.5, 499.5, 149.5, -0.5)

Python

python
plt.figure(figsize=(4,4))
plt.imshow(rot.crop((900,170,1100,320)))
plt.axis('off')

(-0.5, 199.5, 149.5, -0.5)

Python

python
plt.figure(figsize=(4,4))
plt.imshow(rot.crop((720,150,850,320)))
plt.axis('off')

(-0.5, 129.5, 169.5, -0.5)

Python

python
plt.figure(figsize=(10,4))
plt.imshow(rot.crop((350,380,1600,570)))
plt.axis('off')

(-0.5, 1249.5, 189.5, -0.5)

Python

python
plt.figure(figsize=(10,6))
plt.imshow(rot.crop((350,300,1600,800)))
plt.axis('off')

(-0.5, 1249.5, 499.5, -0.5)

Python

python
plt.figure(figsize=(6,3))
plt.imshow(rot.crop((900,300,1400,400)))
plt.axis('off')

(-0.5, 499.5, 99.5, -0.5)

Python

python
plt.figure(figsize=(6,3))
plt.imshow(rot.crop((1400,300,1800,400)))
plt.axis('off')

(-0.5, 399.5, 99.5, -0.5)

Python

python
plt.figure(figsize=(6,6))
plt.imshow(rot.crop((350,500,800,1100)))
plt.axis('off')

(-0.5, 449.5, 599.5, -0.5)

Python

python
plt.figure(figsize=(6,6))
plt.imshow(rot.crop((800,470,1600,1100)))
plt.axis('off')

(-0.5, 799.5, 629.5, -0.5)

Python

python
plt.figure(figsize=(10,3))
plt.imshow(rot.crop((350,900,1600,1100)))
plt.axis('off')

(-0.5, 1249.5, 199.5, -0.5)

Python

python
plt.figure(figsize=(10,6))
plt.imshow(rot2)
plt.axis('off')

(-0.5, 1919.5, 1079.5, -0.5)

Python

python
rot2_flip = rot2.rotate(180, expand=True)
plt.figure(figsize=(10,6))
plt.imshow(rot2_flip)
plt.axis('off')

(-0.5, 1919.5, 1079.5, -0.5)

Python

python
plt.figure(figsize=(6,6))
plt.imshow(rot.crop((0,300,350,750)))
plt.axis('off')

(-0.5, 349.5, 449.5, -0.5)

Python

python
plt.figure(figsize=(8,6))
plt.imshow(rot.crop((300,270,550,500)))
plt.axis('off')

(-0.5, 249.5, 229.5, -0.5)

Python

python
plt.figure(figsize=(8,8))
plt.imshow(rot.crop((300,300,850,800)))
plt.axis('off')

(-0.5, 549.5, 499.5, -0.5)

Python

python
plt.figure(figsize=(8,8))
plt.imshow(rot.crop((300,800,850,1100)))
plt.axis('off')

(-0.5, 549.5, 299.5, -0.5)

Python

python
plt.figure(figsize=(8,4))
plt.imshow(rot.crop((700,430,1600,570)))
plt.axis('off')

(-0.5, 899.5, 139.5, -0.5)

Python

python
plt.figure(figsize=(8,4))
plt.imshow(rot.crop((800,540,1600,700)))
plt.axis('off')

(-0.5, 799.5, 159.5, -0.5)

Python

python
plt.imshow(rot.crop((650,540,1050,700)))
plt.axis('off')

(-0.5, 399.5, 159.5, -0.5)

Python

python
plt.figure(figsize=(8,4))
plt.imshow(rot.crop((300,540,700,700)))
plt.axis('off')

(-0.5, 399.5, 159.5, -0.5)

Python

python
plt.imshow(rot.crop((300,665,700,800)))
plt.axis('off')

(-0.5, 399.5, 134.5, -0.5)

Python

python
plt.imshow(rot.crop((300,770,700,920)))
plt.axis('off')

(-0.5, 399.5, 149.5, -0.5)

Python

python
plt.figure(figsize=(10,4))
plt.imshow(rot.crop((400,920,1600,1040)))
plt.axis('off')

(-0.5, 1199.5, 119.5, -0.5)

Python

python
import pytesseract
import cv2
import numpy as np

# convert to grayscale, threshold maybe
gray = cv2.cvtColor(np.array(rot), cv2.COLOR_BGR2GRAY)
text = pytesseract.image_to_string(gray, lang='chi_sim+eng')
text

'EhEd tb 54 (49)\n\n=\nA sheath\nOe HOM, — gy 8 |\nLief\n\n‘te eb, koe\n\nme, Hie6\n\nAnker, is\n#4\n\neared ni oe Las ae eal ) =\nNas ‘ns\n\nst!\n'

Python

python
plt.figure(figsize=(4,4))
plt.imshow(rot.crop((600,150,650,300)))
plt.axis('off')

(-0.5, 49.5, 149.5, -0.5)

Python

python
plt.figure(figsize=(4,4))
plt.imshow(rot.crop((650,150,800,300)))
plt.axis('off')

(-0.5, 149.5, 149.5, -0.5)

Python

python
plt.imshow(rot.crop((800,160,940,310)))
plt.axis('off')

(-0.5, 139.5, 149.5, -0.5)

Python

python
plt.imshow(rot.crop((780,170,840,320)))
plt.axis('off')

(-0.5, 59.5, 149.5, -0.5)

Python

python
plt.imshow(rot.crop((830,160,930,300)))
plt.axis('off')

(-0.5, 99.5, 139.5, -0.5)

Python

python
plt.imshow(rot.crop((850,150,1000,320)))
plt.axis('off')

(-0.5, 149.5, 169.5, -0.5)

Python

python
plt.figure(figsize=(10,4))
plt.imshow(rot.crop((600,100,1100,320)))
plt.axis('off')

(-0.5, 499.5, 219.5, -0.5)

Python

python
plt.imshow(rot.crop((910,130,990,310)))
plt.axis('off')

(-0.5, 79.5, 179.5, -0.5)

Python

python
plt.figure(figsize=(4,4))
plt.imshow(rot.crop((880,140,990,260)))
plt.axis('off')

(-0.5, 109.5, 119.5, -0.5)

Python

python
plt.figure(figsize=(6,4))
plt.imshow(rot.crop((830,150,1050,310)))
plt.axis('off')

(-0.5, 219.5, 159.5, -0.5)

Python

python
plt.imshow(rot.crop((760,150,860,300)))
plt.axis('off')

(-0.5, 99.5, 149.5, -0.5)

Pensamento

orig = Image.open('/mnt/data/09b816fe5c184d8a27fb25890fead355.jpg') plt.figure(figsize=(6,10)) plt.imshow(orig) plt.axis('off')

Thought for 5m 38s

Resposta

下面是按照从上到下、从左到右的顺序，对照片中能辨认出的全部文字 ——包括标题、分栏标题及条目 ——的逐字转录。由于原稿笔迹较潦草，个别字句我用 □ 标记，表示“无法确认但位置上有字”；若能大致判断含义，则在方括号里附上推测，供你核对。

text
生而全球　□来□分（4 类）

大制造业颠覆式　│　软件及互联网势能　│　品牌化智造　│　□（标题不清）

好奇心驱动　极客科普　炫、趣、拆  
供应中枢  
中国企业  
车：小鹏 XPeng，理想 Li Auto，□（比亚迪?）  
电池：宁德时代  CATL  
（空行）
Google，亚马逊，苹果，  
腾讯，阿里巴巴  
Anker，安克创新  
DJI 大疆，Roborock 石头科技  
（以下数行字迹大量涂改，无法辨认）  

—— 未来全球化的每一种商业逻辑，大体都能被拆解为以上赛道的组合与迭代

说明 & 建议

  1. □ 的部分 若你能回忆或辨认原意，可自行替换。
  2. 如果需要更精确的 OCR 结果，建议：
    • 用手机正对页面、光线均匀地再拍一张；
    • 或者把原图导入电脑，用图像增强（对比度、锐化）后再识别。
  3. 若要把这页笔记整理成干净的电子文档，告诉我格式（Word / Markdown 等），我可以帮你排版。

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