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    <title>DEV Community: Andriy Kuzmenko</title>
    <description>The latest articles on DEV Community by Andriy Kuzmenko (@andriyvk).</description>
    <link>https://dev.to/andriyvk</link>
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      <title>DEV Community: Andriy Kuzmenko</title>
      <link>https://dev.to/andriyvk</link>
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    <item>
      <title>Ukrainian scanned PDFs to Markdown for RAG and LLMs</title>
      <dc:creator>Andriy Kuzmenko</dc:creator>
      <pubDate>Sat, 07 Nov 2026 00:31:17 +0000</pubDate>
      <link>https://dev.to/andriyvk/ukrainian-scanned-pdfs-to-markdown-for-rag-and-llms-ala</link>
      <guid>https://dev.to/andriyvk/ukrainian-scanned-pdfs-to-markdown-for-rag-and-llms-ala</guid>
      <description>&lt;p&gt;Most document-to-text tools are tuned for English. A scanned Ukrainian invoice or court decision often comes back&lt;br&gt;
as Latin look-alike gibberish, or with its tables lost. This guide shows a pipeline that keeps Cyrillic text,&lt;br&gt;
headings and tables, and shows exactly what it does and does not do - every output below is from a real run on&lt;br&gt;
6 November 2026.&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;&lt;strong&gt;Update, 7 November 2026.&lt;/strong&gt; The first run showed two problems: a table in a scan came back as one flat row, and&lt;br&gt;
&lt;code&gt;["ukr", "eng"]&lt;/code&gt; turned «Арматура» into &lt;code&gt;Apmatypa&lt;/code&gt;. Both are fixed now; the outputs below are from the fixed&lt;br&gt;
version (build 0.1.14), and the old ones are kept in the text so you can see the difference.&lt;/p&gt;

&lt;p&gt;Disclosure: I built the tool used here, &lt;a href="https://apify.com/ai_admin/document-to-markdown" rel="noopener noreferrer"&gt;Document to Markdown for AI&lt;/a&gt;&lt;br&gt;
on Apify. Its source code is open: &lt;a href="https://github.com/AndriyVK/-apify-document-tools" rel="noopener noreferrer"&gt;AndriyVK/-apify-document-tools&lt;/a&gt;.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;h2&gt;
  
  
  What you get
&lt;/h2&gt;

&lt;p&gt;One record per document: the Markdown, every table as rows of cells, optional chunks with their nearest heading&lt;br&gt;
(ready for a vector database), and page counts. PDF pages with a text layer are read directly; scanned pages and&lt;br&gt;
images go through Tesseract OCR, which has a proper Ukrainian model.&lt;/p&gt;

&lt;p&gt;Formats: PDF, scanned PDF, PNG/JPG/TIFF, DOCX/DOC, XLSX/XLS, PPTX/PPT, ODT/ODS/ODP, RTF, HTML.&lt;/p&gt;
&lt;h2&gt;
  
  
  Run it from Python
&lt;/h2&gt;


&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight shell"&gt;&lt;code&gt;pip &lt;span class="nb"&gt;install &lt;/span&gt;apify-client
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight python"&gt;&lt;code&gt;&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="n"&gt;apify_client&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;ApifyClient&lt;/span&gt;

&lt;span class="n"&gt;client&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nc"&gt;ApifyClient&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;&amp;lt;YOUR_APIFY_TOKEN&amp;gt;&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;run&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;client&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;actor&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;ai_admin/document-to-markdown&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;).&lt;/span&gt;&lt;span class="nf"&gt;call&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;run_input&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;{&lt;/span&gt;
    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;sources&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;
        &lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;url&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;https://api.apify.com/v2/key-value-stores/FloomuI2DrQCJazY8/records/scan-uk.pdf&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;},&lt;/span&gt;
        &lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;url&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;https://api.apify.com/v2/key-value-stores/FloomuI2DrQCJazY8/records/sheet-uk.xlsx&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;},&lt;/span&gt;
    &lt;span class="p"&gt;],&lt;/span&gt;
    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;ocrMode&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;auto&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;          &lt;span class="c1"&gt;# OCR only pages without a text layer
&lt;/span&gt;    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;ocrLanguages&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;ukr&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;    &lt;span class="c1"&gt;# list only the languages that are really in the scan
&lt;/span&gt;    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;includeTables&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="bp"&gt;True&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
    &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;chunkSize&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="mi"&gt;800&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;           &lt;span class="c1"&gt;# characters per chunk, 0 = no chunks
&lt;/span&gt;&lt;span class="p"&gt;})&lt;/span&gt;
&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;item&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;client&lt;/span&gt;&lt;span class="p"&gt;.&lt;/span&gt;&lt;span class="nf"&gt;dataset&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;run&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;defaultDatasetId&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;]).&lt;/span&gt;&lt;span class="nf"&gt;iterate_items&lt;/span&gt;&lt;span class="p"&gt;():&lt;/span&gt;
    &lt;span class="nf"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;item&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;fileName&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;item&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;ocrPages&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;OCR pages&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="nf"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;item&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="s"&gt;markdown&lt;/span&gt;&lt;span class="sh"&gt;"&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;


&lt;p&gt;The two sample files are public, so you can run this as is. Your own files work by URL, or from your Apify&lt;br&gt;
key-value store.&lt;/p&gt;
&lt;h2&gt;
  
  
  The real output
&lt;/h2&gt;

&lt;p&gt;&lt;strong&gt;A one-page scan&lt;/strong&gt; (&lt;code&gt;scan-uk.pdf&lt;/code&gt;, no text layer - 1 OCR page):&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight plaintext"&gt;&lt;code&gt;Звіт про постачання

Склад у Львові отримав 1240 палет будівельних матеріалів протягом

третього кварталу. Терміни доставки скоротилися на дванадцять відсотків після запровадження

нового графіка.

| Товар | Кількість | Ціна |
| --- | --- | --- |
| Цемент | 420 | 96,50 |
| Арматура | 180 | 412,00 |
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;The text is right, letter for letter, and the ruled table is a real table again - in the Markdown and as JSON rows&lt;br&gt;
&lt;code&gt;[["Товар", "Кількість", "Ціна"], ["Цемент", "420", "96,50"], ["Арматура", "180", "412,00"]]&lt;/code&gt;. On 6 November the&lt;br&gt;
same scan gave one flat row: &lt;code&gt;| Товар | Кількість | Ціна | Цемент | 420 | 96,50 | Арматура | 180 | 412,00&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The same content as an Excel file&lt;/strong&gt; (&lt;code&gt;sheet-uk.xlsx&lt;/code&gt;, 0.2 s) - here the structure is exact:&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight plaintext"&gt;&lt;code&gt;## Data

| Товар | Кількість | Ціна |
| --- | --- | --- |
| Цемент | 420 | 96,50 |
| Арматура | 180 | 412,00 |
| Total | 600 | 1,234.50 |
&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;Cell values are kept as written: &lt;code&gt;96,50&lt;/code&gt; stays &lt;code&gt;96,50&lt;/code&gt;, it is not turned into a float.&lt;/p&gt;

&lt;h2&gt;
  
  
  One setting that matters: OCR languages
&lt;/h2&gt;

&lt;p&gt;On 6 November, with &lt;code&gt;"ocrLanguages": ["ukr", "eng"]&lt;/code&gt;, the word «Арматура» came back as &lt;code&gt;Apmatypa&lt;/code&gt; - the OCR picked&lt;br&gt;
Latin letters that look the same. Now such a word is put back in Cyrillic when it is in Tesseract's Ukrainian&lt;br&gt;
dictionary and the Latin spelling is not an English word, so &lt;code&gt;Excel&lt;/code&gt;, &lt;code&gt;API&lt;/code&gt; or &lt;code&gt;copy&lt;/code&gt; stay as they are. The run on&lt;br&gt;
7 November with &lt;code&gt;["ukr", "eng"]&lt;/code&gt; gave «Арматура» correctly. Still, list only the languages that are really on the&lt;br&gt;
page: every extra language is one more way for the OCR to guess wrong.&lt;/p&gt;

&lt;h2&gt;
  
  
  Price
&lt;/h2&gt;

&lt;p&gt;You pay per page: &lt;strong&gt;$1 per 1,000 text pages and $4 per 1,000 OCR pages&lt;/strong&gt; (Apify pay-per-event pricing; OCR is&lt;br&gt;
charged only for pages that needed it). A document that cannot be downloaded or read is not charged. The two files&lt;br&gt;
above cost a fraction of a cent.&lt;/p&gt;

&lt;h2&gt;
  
  
  Limits - honestly
&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;strong&gt;Tables in scans&lt;/strong&gt; come back as tables when they have ruled grid lines and the scan is clean. A borderless table,
or a grid lost in a noisy 200 dpi scan, still comes out as text. If you have the original Word or Excel file,
convert that instead - its tables are exact.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;OCR quality follows the scan.&lt;/strong&gt; Clean 300 dpi scans read well; phone photos at an angle are worse. For photos, the
&lt;a href="https://apify.com/ai_admin/image-to-text-ocr" rel="noopener noreferrer"&gt;Image to Text OCR&lt;/a&gt; Actor auto-rotates pages and returns a confidence
for every word.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Handwriting&lt;/strong&gt; is not supported.&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;
  
  
  Use it from an AI agent
&lt;/h2&gt;

&lt;p&gt;The same tool is available through the Apify MCP server, so Claude, Cursor or any MCP client can call it:&lt;br&gt;
&lt;code&gt;https://mcp.apify.com?tools=ai_admin/document-to-markdown&lt;/code&gt;. A local MCP server with all our tools is in&lt;br&gt;
&lt;a href="https://github.com/AndriyVK/-apify-document-tools/tree/main/mcp-server" rel="noopener noreferrer"&gt;the GitHub repository&lt;/a&gt;.&lt;/p&gt;




&lt;p&gt;&lt;em&gt;Originally published at &lt;a href="https://andriyvk.github.io/ukrainian-scans-to-markdown/" rel="noopener noreferrer"&gt;andriyvk.github.io&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

</description>
      <category>ocr</category>
      <category>python</category>
      <category>rag</category>
      <category>ai</category>
    </item>
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