{"id":847,"date":"2026-02-19T12:24:35","date_gmt":"2026-02-19T12:24:35","guid":{"rendered":"https:\/\/blog.nimail.cn\/?p=847"},"modified":"2026-02-19T12:24:35","modified_gmt":"2026-02-19T12:24:35","slug":"2026%e8%85%be%e8%ae%af%e5%ae%9e%e6%b5%8b-%e5%9b%be%e7%89%87%e8%bd%ac%e6%88%90excel%e5%85%a8%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/blog.nimail.cn\/best-articles\/2026%e8%85%be%e8%ae%af%e5%ae%9e%e6%b5%8b-%e5%9b%be%e7%89%87%e8%bd%ac%e6%88%90excel%e5%85%a8%e6%8c%87%e5%8d%97\/","title":{"rendered":"2026\u817e\u8baf\u5b9e\u6d4b\uff1a\u56fe\u7247\u8f6c\u6210Excel\u5168\u6307\u5357"},"content":{"rendered":"

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\u5e38\u89c1\u65b9\u5f0f\u4e0e\u9002\u7528\u573a\u666f \u5f00\u53d1\u8005\u63a8\u8350<\/span><\/h3>\n
    \n
  • \u5728\u7ebf\u5de5\u5177\uff08\u5feb\u6377\uff09<\/strong>\uff1a\u9002\u5408\u5feb\u901f\u628a\u56fe\u7247\u8f6c\u6210excel<\/strong>\u3001\u56fe\u7247\u8f6c\u8868\u683c\u5728\u7ebf\u514d\u8d39\u6216\u56fe\u7247\u8f6cexcel\u5728\u7ebf\u514d\u8d39\u8f6c\u6362\u3002\u4f18\u70b9\u662f\u96f6\u90e8\u7f72\uff0c\u7f3a\u70b9\u662f\u9690\u79c1\u9700\u6ce8\u610f\u3002\u793a\u4f8b\uff1a\u4e0a\u6587\u6848\u4f8b\u9875\u9762\u652f\u6301jpg excel\u4e00\u952e\u63d0\u53d6\u3002<\/li>\n
  • \u624b\u673a\/\u5fae\u4fe1\u5c0f\u5de5\u5177<\/strong>\uff1a\u4fbf\u6377\uff0c\u9002\u7528\u4e8e\u5fae\u4fe1\u56fe\u7247\u8f6c\u8868\u683c\u6216\u5fae\u4fe1\u56fe\u7247\u8f6cexcel\uff0c\u4f46\u590d\u6742\u8868\u683c\u8bc6\u522b\u7387\u53d7\u9650\u3002<\/li>\n
  • \u672c\u5730Python\u811a\u672c\uff08\u53ef\u5b9a\u5236\uff09<\/strong>\uff1a\u5f53\u9700\u8981\u6279\u91cf\u5904\u7406\u3001\u5927\u91cf\u8868\u683c\u63d0\u53d6\u6216\u590d\u6742\u6570\u636e\u6e05\u6d17\u65f6\uff0c\u7528pytesseract+OpenCV+Pandas\u6700\u7075\u6d3b\uff0c\u80fd\u5b9e\u73b0\u56fe\u7247\u8bc6\u522b\u8868\u683c\u5e76\u5bfc\u51fa\u4e3aExcel\u3002<\/li>\n<\/ul>\n
    \n \u5c0f\u63d0\u793a\uff1a\u5982\u679c\u662f\u53d1\u7968\u6216\u654f\u611f\u6570\u636e\uff0c\u4f18\u5148\u9009\u62e9\u672c\u5730\u811a\u672c\u6216\u4f01\u4e1a\u7ea7\u670d\u52a1\uff0c\u907f\u514d\u628a\u9690\u79c1\u56fe\u7247\u4e0a\u4f20\u5230\u516c\u5171\u5728\u7ebf\u56fe\u7247\u8f6c\u8868\u683c\u5de5\u5177\u3002\n<\/div>\n

    \u64cd\u4f5c\u6b65\u9aa4\uff08\u4ee5\u5728\u7ebf\u5de5\u5177\u4e3a\u4f8b\uff09<\/h3>\n
      \n
    1. \u6253\u5f00\u5728\u7ebf\u56fe\u7247\u8f6cExcel\u9875\u9762\uff08\u4f8b\u5982\u4e0a\u9762\u94fe\u63a5\uff09\u3002<\/li>\n
    2. \u4e0a\u4f20jpg\/png\u7b49\u7167\u7247\uff0c\u6216\u76f4\u63a5\u62d6\u62fd\u5fae\u4fe1\u622a\u56fe\u3002<\/li>\n
    3. \u7cfb\u7edf\u81ea\u52a8\u8fdb\u884c\u56fe\u7247\u8bc6\u522b\u8868\u683c\uff08OCR + \u8868\u683c\u5b9a\u4f4d\uff09\uff0c\u9884\u89c8\u8bc6\u522b\u7ed3\u679c\u3002<\/li>\n
    4. \u786e\u8ba4\u5e76\u5bfc\u51fa\u4e3aExcel\u6216CSV\uff0c\u53e6\u53ef\u8fdb\u884c\u8868\u5934\u6821\u6b63\u4e0e\u5355\u5143\u683c\u5408\u5e76\u5904\u7406\u3002<\/li>\n<\/ol>\n

      \u5e38\u89c1\u95ee\u9898\uff08FAQ\uff09<\/h4>\n
        \n
      • \u8bc6\u522b\u9519\u8bef\u591a\uff1a\u8bd5\u7740\u63d0\u5347\u56fe\u7247\u6e05\u6670\u5ea6\u6216\u88c1\u5207\u6389\u65e0\u5173\u533a\u57df\u3002<\/li>\n
      • \u8868\u683c\u8fb9\u6846\u4e0d\u660e\u663e\uff1a\u53ef\u5148\u7528\u672c\u5730\u5de5\u5177\u589e\u5f3a\u5bf9\u6bd4\u5ea6\u518d\u4e0a\u4f20\u3002<\/li>\n
      • \u5fae\u4fe1\u56fe\u7247\u8f6cexcel\u5931\u8d25\uff1a\u5efa\u8bae\u628a\u56fe\u7247\u4fdd\u5b58\u5230\u672c\u5730\u518d\u4e0a\u4f20\uff0c\u6216\u4f7f\u7528\u7f51\u9875\u7248\u7684\u8868\u683c\u63d0\u53d6\u529f\u80fd\u3002<\/li>\n<\/ul>\n
        \n
        \n
        \u793a\u4f8b\uff1aPython \u5c06\u56fe\u7247\u8f6c\u4e3a Excel<\/h5>\n

        \u4e0b\u9762\u662f\u4e00\u4e2a\u57fa\u7840\u7684\u793a\u4f8b\uff0c\u4f7f\u7528 pytesseract \u63d0\u53d6\u6587\u5b57\u5e76\u7528 pandas \u62fc\u63a5\u6210\u8868\u683c\u540e\u5199\u5165 Excel\u3002\u9002\u5408\u89c4\u5219\u5316\u7684\u8868\u683c\u56fe\u7247\u5feb\u901f\u5b9e\u73b0\u56fe\u7247\u8f6cexcel\u3002<\/p>\n

        # \u4f9d\u8d56: pytesseract, opencv-python, pandas, openpyxl\nimport cv2\nimport pytesseract\nimport pandas as pd\n\nimg = cv2.imread('table.jpg')\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n# \u7b80\u5355\u4e8c\u503c\u5316\uff0c\u63d0\u5347\u8bc6\u522b\u7387\n_, th = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)\ntext = pytesseract.image_to_string(th, config='--psm 6')\n# \u5047\u8bbe\u4ee5\u6362\u884c\u4e3a\u884c\u3001\u5236\u8868\u7b26\u6216\u591a\u4e2a\u7a7a\u683c\u5206\u5217\nrows = [r.strip() for r in text.split('\\n') if r.strip()]\ndata = [re.split(r'\\s{2,}|\\t', r) for r in rows]\ndf = pd.DataFrame(data)\ndf.to_excel('output.xlsx', index=False)\n<\/code><\/pre>\n
            \u6ce8\uff1a\u590d\u6742\u8868\u683c\u5efa\u8bae\u7ed3\u5408\u8868\u683c\u68c0\u6d4b\uff08line\/contour\uff09\u4e0e\u5355\u5143\u683c\u5408\u5e76\u903b\u8f91\u3002<\/small>\n  <\/div>\n<\/div>\n
        \u8bc6\u522b\u8d28\u91cf\u4f18\u5316\u4e0e\u5bf9\u6bd4<\/h2>\n
        \u4ece\u5b9e\u8df5\u6765\u770b\uff0c\u56fe\u7247\u8bc6\u522b\u8868\u683c<\/strong>\u7684\u5173\u952e\u5728\u4e8e\u56fe\u7247\u8d28\u91cf\u3001\u8868\u683c\u7ebf\u6761\u6e05\u6670\u5ea6\u4ee5\u53ca\u6587\u5b57\u8bed\u8a00\u6a21\u578b\u3002\u5728\u7ebf\u5de5\u5177\uff08\u4f8b\u5982\u4e0a\u6587\u793a\u4f8b\u94fe\u63a5\uff09\u5728\u666e\u901a\u53d1\u7968\/\u529e\u516c\u8868\u683c\u573a\u666f\u4e0b\u63d0\u4f9b\u4e86\u4e0d\u9519\u7684\u8bc6\u522b\u7387\uff0c\u4f46\u5bf9\u4e8e\u5408\u5e76\u5355\u5143\u683c\u6216\u624b\u5199\u5185\u5bb9\uff0c\u8fd8\u662f\u5efa\u8bae\u672c\u5730\u5b9a\u5236\u5316\u6d41\u7a0b\u3002<\/p>\n
        \n\n\n\n\n\n\n\n
        \u65b9\u6848<\/th>\n\u4f18\u70b9<\/th>\n\u9002\u7528\u573a\u666f<\/th>\n<\/tr>\n<\/thead>\n
        \u5728\u7ebf\u5de5\u5177\uff08\u5982 nimail\uff09<\/td>\n\u5feb\u901f\u3001\u65e0\u9700\u90e8\u7f72\u3001\u652f\u6301jpg\u8f6cexcel<\/td>\n\u4e00\u6b21\u6027\u8f6c\u6362\u3001\u5fae\u4fe1\u56fe\u7247\u8f6cexcel\u3001\u514d\u8d39\u56fe\u7247\u8f6cexcel\u6d4b\u8bd5<\/td>\n<\/tr>\n
        Python \u672c\u5730\u811a\u672c<\/td>\n\u53ef\u5b9a\u5236\u3001\u9690\u79c1\u9ad8\u3001\u9002\u5408\u6279\u91cf<\/td>\n\u590d\u6742\u8868\u683c\u3001\u81ea\u52a8\u5316\u6d41\u6c34\u7ebf\u3001\u8868\u683c\u63d0\u53d6<\/td>\n<\/tr>\n
        \u624b\u673aAPP\/\u5c0f\u7a0b\u5e8f<\/td>\n\u4fbf\u6377\u3001\u5bf9\u63a5\u5fae\u4fe1\u6d41\u91cf<\/td>\n\u626b\u7801\/\u62cd\u7167\u5373\u65f6\u8f6c\u6362<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
        \n  \u5982\u679c\u4f60\u60f3\u8981\u4e00\u4e2a\u514d\u8d39\u56fe\u7247\u8f6cexcel<\/strong>\u7684\u8bd5\u7528\u8def\u5f84\uff0c\u53ef\u4ee5\u5148\u7528\u5728\u7ebf\u56fe\u7247\u8f6c\u8868\u683c\u5de5\u5177\u5feb\u901f\u9a8c\u8bc1\uff08\u4f8b\u5982\u4e0a\u9762\u94fe\u63a5\uff09\uff0c\u518d\u6839\u636e\u8bc6\u522b\u8d28\u91cf\u51b3\u5b9a\u662f\u5426\u8d70\u81ea\u52a8\u5316\u811a\u672c\u5b9e\u73b0\u6279\u91cf\u5316\u7684\u56fe\u7247\u63d0\u53d6\u8868\u683c\u6d41\u7a0b\u3002\n<\/div>\n
        \u5728\u5b9e\u9645\u5de5\u7a0b\u4e2d\uff0c\u6211\u5e38\u628a\u5728\u7ebf\u5de5\u5177\u4f5c\u4e3a\u539f\u578b\u9a8c\u8bc1\uff08\u56fe\u7247\u8f6c\u6362\u8868\u683c\u3001\u56fe\u7247\u751f\u6210\u8868\u683c\uff09\uff0c\u786e\u8ba4\u53ef\u884c\u540e\u518d\u628a\u8bc6\u522b\u903b\u8f91\u7528Python\u6216\u670d\u52a1\u5316\u65b9\u5f0f\u7a33\u5b9a\u8f93\u51fa\u2014\u2014\u8fd9\u6837\u65e2\u8282\u7701\u65f6\u95f4\uff0c\u4e5f\u80fd\u9488\u5bf9\u8868\u683c\u56fe\u7247\u8f6cexcel\u7684\u75db\u70b9\u505a\u6df1\u5ea6\u4f18\u5316\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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