Parsyra Conversion Examples,
Unedited

Every converter website says its output is clean. This page shows you ours instead. Below are eleven Parsyra conversion examples produced by the exact code that runs when you drop a file on the homepage: plain and financial PDFs, a scanned delivery note, a maths handout, a webpage, a note full of LaTeX, an engineering note full of Mermaid diagrams, a PPTX deck and an Excel workbook — with the report and the handout each compared twice, once through standard Parsyra conversion and once through High-accuracy OCR. Nothing was retouched. Where the result is strong we say so, and where it falls apart — PDF tables, equations, flattened slide tables — we show the failure and explain it.

All input files were authored by us for this page, so you can compare them freely. Every output below is the verbatim result of a single conversion run, with nothing corrected.

// recommended setups

Which Setup Fits Which Job

The converter deliberately has few knobs. These are the setups behind the Parsyra conversion examples above, and when to reach for each one.

Default server run

What every example on this page used unless noted: drop the file, read the result. The current converter sends supported files to the self-hosted Parsyra service for temporary processing. Best for DOCX, XLSX, CSV, HTML and digital-born single-column PDFs.

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AI OCR for scans and math

The high-accuracy path for scanned PDFs. When a PDF has no usable text layer the widget flags it and offers OCR; it rebuilds tables that ordinary text extraction flattens and returns equations as LaTeX — compare the two maths tabs in the gallery to see the difference. Requires sign-in: 10 pages a month free, 500 on Pro.

📊

Tables: start from the source

The financial-report example shows why: PDF tables arrive as text lines, spreadsheet tables arrive as pipe tables. If the numbers exist in XLSX or CSV anywhere upstream, convert that file and skip the PDF entirely. Paste-a-table mode does the same for data on your clipboard.

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Heading to Obsidian

The Markdown body is standard GFM, so it drops into a vault untouched. The Export to Obsidian button additionally writes source, date and tag front-matter and a vault-safe filename. Tables from Excel and webpages render in Obsidian exactly as shown above.

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Heading to GitHub

Output is GitHub-Flavored Markdown already — pipe tables, fenced code blocks and blockquotes in the examples above render on GitHub without changes. Copy the result straight into a README or an issue; there is no separate mode to configure.

// how these were made

How These Parsyra Conversion Examples Were Produced

A quality page is only useful if you can trust its samples. Here is how these Parsyra conversion examples were produced — dull on purpose, so you could repeat it yourself and get the same bytes. Samples produced 30 July 2026.

01

We Authored the Inputs

Every input file was written for this page — a memo, a results summary, a delivery note, a maths handout, a blog post, an architecture note, a deck and a workbook — so there are no copyright questions and nothing was cherry-picked from files we knew would convert well. The generator scripts live in the site's repository.

02

One Run Through the Real Converter

Each file went through the public widget exactly once — the same code path you get, not a lab build. The three scanned samples used High-accuracy OCR; every other file used the default server run with no extra options.

03

Output Pasted Verbatim

What you see in each output panel is the copied result of that run. We fixed nothing: the flattened financial tables, the exploded equations and the collapsed PPTX table are all shown as they came out, because that is the information you actually need before trusting a converter.

04

Then We Annotated

The strengths and limitations under each example are our reading of the output, written after the fact. Where a weakness has a workaround — spreadsheet sources for tables, OCR for scans and columns — the annotation says so instead of hiding the weakness.

Parsyra Conversion Examples — Questions

What these samples do and don't prove, and how to reproduce them.

Are these Parsyra conversion examples really unedited?

Yes. Each output panel is the verbatim clipboard result of one conversion run — including the failures. The financial tables arrive as loose lines rather than a grid, the equations in the maths handout fall apart into scattered glyphs, and the slide table collapses to one value per line, all on public display. The only editorial act anywhere on the page is annotation around the output, never changes inside it.

Why show examples where the converter does badly?

Because you'll find out anyway, five minutes after you rely on it. Showing the flattened PDF tables here costs us a little polish and saves you a surprise. It also makes the good cases believable: when the Excel example says every value lands in the right cell, you know that claim survived the same no-retouching rule as the failures did.

Can I reproduce these results myself?

Yes, and we'd encourage it. The sample files and the scripts that generate them are committed to the site's repository, and the converter on the homepage is the same code that produced these outputs. Author a similar file, run it through, and compare — same input, same bytes out.

Why do PDF tables come out as text lines instead of Markdown tables?

A PDF stores a table as ruled lines plus positioned glyphs; nothing in the format marks where a cell begins or ends. Tools that promise perfect grids from that are guessing from pixel coordinates, and the guess eventually shifts a column silently. We'd rather hand you correct text than a confident wrong table — and point you at the spreadsheet source or AI OCR when the grid itself matters.

Can it convert equations to LaTeX?

Through High-accuracy OCR, yes, and the two maths tabs in the gallery show both sides of it. The standard Parsyra PDF path reads a text layer where an equation is stored as scattered positioned glyphs, so subscripts and summation bounds break onto their own lines — no tool reading that layer can reassemble them reliably. Reading the rendered page visually instead returns proper LaTeX in dollar delimiters. Because that route is offered only for PDFs with no text layer, a born-digital paper has to be flattened to an image first.

What do the AI OCR examples add?

Two of the Parsyra conversion examples in the gallery use the high-accuracy OCR path, and each answers a different doubt. The scanned delivery note rebuilds a table that ordinary text extraction flattens into text lines; the maths handout appears twice, converted normally and then with visual OCR, where the equations return as real LaTeX. Reading the rendered page rather than a text layer is what makes both possible. The path is metered at 10 pages a month free and 500 on Pro.

Do I need an account to get the results shown here?

Most examples on this page use the standard Parsyra conversion path. The scanned tabs use High-accuracy OCR, which is metered at 10 pages a month on the Free tier and 500 on Pro.

My file converted worse than these examples. Why?

Most likely your input carries less structure than the sample did. The examples show the pattern: HTML and XLSX convert almost perfectly because structure is in the file; PDFs degrade as layout complexity rises; scans may yield little or no text until you turn on High-accuracy OCR. Check whether your PDF has a selectable text layer, whether it's multi-column, and whether the data exists in a structured format upstream — that diagnosis covers nearly every disappointing result.

Dig deeper into a specific format: PDF, Excel, PowerPoint, Word, web pages, or scanned files via image to Markdown.

Run Your Own Markdown Conversion Example

The Parsyra conversion examples above are the pitch: real output, limits included. Drop your own file into the converter and hold it to the same standard — and if you hit the ceilings, Pro raises the file size, batch and OCR limits.