taxi_expense

识别滴滴打车订单截图,OCR识别文字+坐标,马赛克脱敏目的地,按月生成报销Excel

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Install skill "taxi_expense" with this command: npx skills add kathi-hash/taxi-expense

When user sends taxi order screenshots:

Setup (first time only)

bash ~/.openclaw/workspace/skills/taxi_expense/scripts/setup.sh

Process Screenshots

node ~/.openclaw/workspace/skills/taxi_expense/scripts/process.js <image1> [image2] ...

The script will:

  1. OCR each image with Tesseract.js v4 (uses coordinates to detect text positions)
  2. Parse orders: date, time, start/end point, amount
  3. Smart amount extraction: handles OCR misreads (¥→%, missing ¥, missing decimal)
  4. Filter: only weekday evening rides (after 21:00) qualify for reimbursement
  5. Auto-exclude closed/cancelled orders (amount = ¥0)
  6. White block desensitization on destination address in screenshots (keeps first/last char visible)
  7. Desensitize destination text in Excel (e.g. 古*****门)
  8. Save screenshots to ~/.openclaw/workspace/taxi_expense/screenshots/
  9. Update ~/.openclaw/workspace/taxi_expense/taxi_data.json (auto-dedup by date+amount)
  10. Generate monthly Excel: ~/.openclaw/workspace/taxi_expense/YYYY-MM-taxi_expense.xlsx

Excel Columns

序号 | 日期 | 星期 | 时间 | 起点 | 终点(脱敏) | 金额 | 备注

Sheet 2 contains mosaiced order screenshots.

Output

Tell user:

  • How many new orders were added
  • Monthly totals (reimbursable orders only)
  • Any skipped orders and why (weekend, before 21:00, closed, ¥0)

Send Preview (only if user asks)

The script saves desensitized screenshots to the screenshots/ directory. Send via:

openclaw message send --channel telegram --target <chat_id> --message "msg" --media <file.xlsx>

Known Issues

  • Tesseract Chinese quality is imperfect ("点"→"炭", "轻享"→"轻亭")
  • Uses regex /终[点炭]/ for tolerant matching
  • Amount recognition: ¥ may be misread as % or lost entirely
  • Amount > 500 is auto-corrected (missing decimal: 1970→19.70)
  • Source images must be ORIGINAL screenshots (not previously processed/cropped)
  • Multiple images supported: node process.js img1 img2 img3

Source Transparency

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