AI-Assisted PDF to Word: Review the Converter and Its Checks

The pdf2word project is a small Python desktop application combining PDF-to-DOCX conversion, Word-to-PDF conversion, and a PyQt6 interface. It provides a concrete codebase for examining AI-assisted software work: what a wrapper around a library does, what its post-processing changes, and what its verification script can establish.

For this revision, the local project source, conversion implementation, and verification script were inspected. The original private test PDF and historical run logs were not available for independent reproduction. This article therefore does not claim a measured conversion time, a verified page count, or a universal “zero garbling” result.

Trace the conversion path

The application uses pdf2docx for PDF-to-Word work and LibreOffice for the reverse direction. These are different engines with different requirements. A successful PDF-to-DOCX conversion does not demonstrate that Word-to-PDF works on a machine without LibreOffice.

Inspect the project dependencies and startup scripts before installation. Use a Python environment compatible with the pinned versions in the checked-out release. The repository’s run.sh creates an environment and launches the GUI; read it before executing. A desktop application processing local files can avoid uploading a document for conversion, but check any additional integrations or diagnostics before using confidential inputs.

These screenshots illustrate the project interface. They are not evidence that the present code has passed a new conversion run.

Layout reconstruction needs semantic checks

A PDF usually describes positioned page content rather than the editable structure expected in Word. A conversion can look plausible while producing awkward reading order, fragmented paragraphs, inaccurate tables, or headings that are merely large text.

The project’s post-processing uses heuristics to improve headings and code formatting. Review those rules against document types you actually use. A font-size or numbering rule can misclassify captions as headings. A monospace exclusion can help with code but can also miss legitimate headings. A source-code workaround for one document is a hypothesis to test on another document, not a general accuracy guarantee.

For a code-heavy document, select a few representative pages and compare indentation, punctuation, line wraps, and copyable text. For a business document, compare table boundaries, merged cells, numbering, headers, and footnotes. Separate these observations rather than reducing all quality to a single score.

What verify.py measures

The project’s verification script counts DOCX paragraphs, tables, headings, and characters. Its suspicious-character detector looks for replacement characters, a square glyph, and characters in a private-use range.

A zero count means those markers were not found in the text the script examined. It does not show that all source text survived, that every Chinese character is correct, or that the layout matches. An omitted sentence contributes no suspicious characters. A visually wrong but valid Unicode character may also pass.

The script’s thresholds are document-specific. Requiring many paragraphs, several tables, or a high Chinese-character ratio would reject a valid short English letter. Adjust acceptance criteria to the fixture instead of treating those numbers as a universal converter test.

Create a public, reproducible fixture

Use a document you are allowed to distribute, with known text, headings, a table, code, and deliberate page breaks. Include a scanned-page fixture separately: a PDF without an extractable text layer needs OCR support, which should not be assumed from the conversion library wrapper.

From a configured project environment, the script supports this form:

python verify.py --skip-roundtrip /path/to/public-fixture.pdf

Omitting --skip-roundtrip also attempts LibreOffice conversion. Save the source, generated DOCX, report, versions, and selected rendered comparisons. Keep filenames and report excerpts free of private material when publishing them.

Review AI contributions through artifacts

Without retained prompts and commits, it is not possible to assign each component reliably to a human or an AI assistant. The useful review is the code and its behavior: resource cleanup, subprocess failures, cancellation, output overwrites, and whether a “completed” message corresponds to a usable document.

If you build on this project, add acceptance cases for your document types and retain the outputs. The AI code review guide explains the same principle in Java: an attractive result needs checks matched to the failure modes it can hide. The hallucination guide covers checking proposed APIs before relying on generated implementation details.