Why PDF to Word Conversion Is Harder Than You Think (And How to Do It Right)

“Just convert the PDF to Word.”

If you’ve ever heard that sentence, you’ve probably felt the same thing I did: surely it’s just changing a file extension, right?

Nope.

PDF is not “a Word document with different packaging.” It’s much closer to a page description language: a way to paint text and shapes at exact coordinates. That’s why copy-paste from PDFs can look like a ransom note, and why “perfect PDF→Word” is still one of the most annoying problems in software.

I built (and tested) multiple PDF conversion workflows. This post explains:

• what PDFs actually store under the hood,
• why conversion breaks formatting,
• how different tools compare,
• when OCR is mandatory,
• and the three practical tricks that get you the best results.

1) What a PDF really is (and why that matters)

Most people think a PDF is a “document” like Word: paragraphs, headings, tables, lists.

But PDFs typically don’t store those semantic structures.

PDF stores how to draw a page, not how to understand it

If you crack open a PDF's content stream, this is roughly what you see:

BT
  /F1 12 Tf          % select font F1 at 12pt
  124.2 512.7 Td     % move to coordinates (124.2, 512.7)
  (Hello World) Tj   % draw the string
ET

That's a PDF text operator block. BT/ET = begin/end text. Td = set position. Tj = show string. The PDF literally says "paint these glyphs at these coordinates."

That's great for rendering. It's awful for reconstructing structure.

Word wants:

• paragraphs
• runs
• fonts
• styles
• tables
• numbered lists
• flowing text

PDF often gives you:

• coordinates
• glyph shapes
• embedded fonts (sometimes subset fonts)
• drawing instructions

So conversion becomes: guess the structure from geometry.

Why PDF copy-paste produces weird text

If you’ve ever copied text from a PDF and got:

• missing spaces,
• wrong order,
• random symbols,
• broken lines,

that’s usually because:

• characters are placed independently,
• spacing is inferred,
• fonts are subset/encoded,
• the logical reading order isn’t stored,
• multi-column layout confuses text extraction.

Your eyes understand “this is a paragraph.” The PDF might only know “these glyphs are near each other.”

Scanned PDF vs “native” PDF (the most important distinction)

There are two broad PDF types:

1) Native (text-based) PDF

• text is selectable
• you can highlight words
• conversion can extract actual characters

2) Scanned PDF

• the page is an image (or a series of images)
• text is NOT really text
• conversion without OCR produces junk or blank documents

Before you pick a converter, you should ask one question:
Can I select the text in this PDF?
If not, you’re in OCR territory.

2) Comparing common PDF→Word conversion approaches

I tested a few categories, because they show up in real workflows.

Option A: LibreOffice CLI (free, fast, rough edges)

LibreOffice has a CLI route that can convert documents, and it’s “good enough” for simple PDFs.

Pros

• free
• automatable
• works offline

Cons

• formatting often breaks (especially tables, columns, complex layouts)
• scanned PDFs still require OCR elsewhere
• “good” output is inconsistent across PDFs

If your PDF is a simple single-column report, it can work. If your PDF is a design-heavy layout, it will suffer.

Option B: pdf2docx (Python library)

This is a popular approach for developers: script conversion in a pipeline.

from pdf2docx import Converter

cv = Converter("input.pdf")
cv.convert("output.docx")
cv.close()

Pros

• developer-friendly
• can perform well on simple PDFs
• easy to integrate into backend jobs

Cons

• complex PDFs break quickly (tables, mixed layouts)
• output quality depends heavily on the input structure
• still not a magic solution for scanned PDFs

It’s a great tool when you control the PDF source. It’s less great when users upload random PDFs from everywhere.

Option C: Adobe Acrobat (expensive, but often the most reliable)

Acrobat is the “it usually works” option.

Pros

• generally strong fidelity
• handles complex cases better
• mature OCR features

Cons

• cost (often around $20/month depending on plan)
• not automatable for many teams
• not ideal for quick, occasional conversions

It’s the tool you buy when the output must look perfect and you can justify the subscription.

Option D: Online converters (convenient vs privacy risk)

Online tools exist because the need is universal:

• no install
• works anywhere
• quick for non-technical users

But they also raise a real concern:
What happens to your files after upload?

That’s why if you build an online converter, you have to be explicit about:

• how files are processed,
• retention window,
• deletion policy,
• and whether files are used for anything else.

What I built (and the engineering tradeoffs)

When I built FastlyConvert's PDF converter, the main engineering decisions were:

Server-side processing over client-side WASM. Client-side sounds appealing (no upload = no privacy concern), but PDF parsing is memory-hungry and slow in the browser — especially for large files or scanned docs that need OCR. Server-side gave predictable performance across all PDF types.

Auto-deletion within 24 hours, not instant. Instant deletion sounds better for privacy, but it breaks the user flow — if the download fails or they close the tab, the file is gone. A 24-hour window balances usability with data minimization.

Separate pipelines for native vs scanned PDFs. Routing scanned PDFs through OCR first (instead of one-size-fits-all) significantly improved output quality for both paths.

Here's the simplified comparison table:

Approach	Cost	Setup	Best for	Formatting fidelity	Scanned PDFs
LibreOffice CLI	Free	Medium	simple PDFs, dev pipelines	Low–Medium	Needs OCR
pdf2docx (Python)	Free	Medium	simple PDFs, controlled sources	Medium	Needs OCR
Adobe Acrobat	$$$	Low	high-stakes formatting	High	Yes (with OCR)
Online converter	$–$$	Lowest	fast, anywhere	Medium–High (varies)	Needs OCR / separate flow

The key: the “best” tool depends on whether your PDF is native or scanned, and how important formatting fidelity is.

3) OCR: the lifesaver for scanned PDFs

If the PDF is a scan, conversion without OCR is like trying to “translate” a photo into a Word doc.

You need OCR: Optical Character Recognition.

Tesseract vs commercial OCR

Tesseract

• free
• runs locally
• decent for clean scans

A minimal example:

# Convert a scanned PDF page to searchable text
tesseract scanned-page.png output -l eng --oem 1 --psm 6
# --oem 1 = LSTM neural net mode
# --psm 6 = assume a single uniform block of text

But it can struggle with:

• low-resolution scans,
• skewed pages,
• noisy backgrounds,
• complex layouts,
• mixed languages.

Commercial OCR engines

• usually handle messy scans better,
• have stronger layout detection,
• often support more languages and fonts robustly,
• but cost money and are typically server-based.

When you need OCR (simple rule)

• If you cannot select text in the PDF → you need OCR.
• If text is selectable but messy → OCR might still help (sometimes).

How to improve OCR accuracy (3 practical tips)

1. Resolution matters
   300 DPI is a common baseline for good OCR. Below that, accuracy drops fast.
2. Contrast matters
   Faint gray scans are harder than crisp black text on white background.
3. Language matters
   OCR engines do better when you specify the language instead of guessing.

I’m planning a deeper guide on scanned PDF workflows — follow me here to catch it when it’s published.

4) Three tricks to keep formatting sane

“Perfect conversion” is rare. But good workflows exist.

Here are three techniques that save the most time.

Trick #1: Table-heavy PDFs → consider Excel first

If the PDF is basically tables (invoices, financial statements, reports), PDF→Word often produces:

• broken cells,
• misaligned columns,
• weird spacing.

In those cases, convert to Excel first, clean the table there, then paste into Word if needed. Dedicated PDF→Excel tools handle tabular extraction much better than PDF→Word tools do.

Trick #2: Mixed text + images → choose higher fidelity over smaller output

Layout-heavy PDFs (brochures, designed resumes, marketing pages) are the hardest.

What helps:

• choosing a “high fidelity” or “preserve layout” mode (when the tool offers it),
• accepting that some edits will be manual,
• and avoiding “over-compression” before conversion.

If your PDF is huge, compress it after you confirm conversion quality — not before.

Trick #3: Pure text documents → TXT can be cleaner than DOCX

This sounds weird, but it’s true:

If the PDF is mostly text and your goal is “editable content,” sometimes extracting plain text gives you a cleaner starting point than a messy DOCX.

DOCX conversion tries to reconstruct layout. TXT skips layout and gives you content.

Then you can restyle it in Word/Docs the way you actually want.

5) Batch processing: where PDF conversion becomes a real workflow problem

Once you move beyond “one PDF,” the pain multiplies.

Scenario 1: HR receives 200 PDF resumes

They need:

• names,
• job titles,
• years of experience,
• searchable keywords.

If half of those resumes are scanned PDFs, they need OCR in the pipeline.

A practical workflow is:

1. Identify scanned vs native (selectable text test)
2. OCR scanned resumes
3. Convert to Word or extract text
4. Index/search the output

Scenario 2: Legal teams archiving contracts

Legal teams care about:

• text search,
• consistent naming,
• archivable formats,
• retention policy.

They may not need perfect formatting, but they do need reliability.

That’s why tool “maturity” matters:

• stable conversion,
• predictable output,
• clear file retention policy,
• and support for batch/bulk handling.

Privacy note (server processing)

FastlyConvert processes PDFs on the server to generate the converted DOCX/XLSX outputs. Files are uploaded temporarily for processing and automatically deleted within 24 hours. Transfers use HTTPS encryption. Please upload only files you own or have permission to use.

What’s your go-to PDF conversion workflow? Have you tried scripting it with pdf2docx or Tesseract, or do you just throw money at Acrobat? I’m curious what other devs rely on — drop your setup in the comments.