My PDF converter turned every borderless table into loose numbers. The fix runs on a CPU.

说明与用途

PDF 转换器最初未能保留表格结构，导致数字分散且段落被破坏。通过集成 Camelot 处理无边框表格、拆分多行单元格以及使用 Tesseract OCR 处理扫描页面，现已准确生成 Markdown 表格并格式化列表，单 CPU 23 秒完成，费用仅 $0.001。

问题

The initial PDF converter failed to preserve table structure, producing loose numbers and broken paragraphs, especially for borderless tables, dense budget tables, scanned pages, and two‑column articles.

结果

The updated converter accurately extracts tables and scanned content, achieving near‑ML quality for key tables while remaining inexpensive ($2 per 1,000 pages) and fast (23 s on one CPU).

工作流

• Run Actor via Apify API
• Use Camelot stream mode on numeric pages
• Split multi‑line cells into rows
• Rebuild Markdown from Tesseract OCR for scanned pages
• Map OCR bullet characters to dashes
• Discard non‑numeric two‑column prose
• Return Markdown chunks with metadata

证据

My converter lost on the two things that mattered. On both tables the cells came out one per line, so the budget became a long column of loose numbers.

来源

Camelot's "stream" mode (MIT licensed) finds those columns from the spacing alone, in 0.1 to 0.3 seconds a page on one CPU.

来源

After the changes, on the same four files: three tables on the two-page borderless report, the budget as one nine-column table with the same rows as the ML converter, the scanned page with its list and headings. All of it took 23 seconds on one CPU, 21 of them OCR.

来源