Overview

Most PDFs contain selectable text — characters stored as font glyphs with known positions. PDF4LLM extracts these directly, without OCR, at high speed. Some PDFs don’t. A document scanned on a photocopier, a fax saved as PDF, or a report exported from a system that rasterises each page before writing it — these contain no machine-readable text at all. Every page is just an image. Native extraction returns empty strings. For these documents, PDF4LLM can invoke Tesseract OCR before running layout analysis. Pass useOcr: true to any extraction method and Tesseract will read the text from each page image before it is converted to Markdown, plain text, or JSON.

Prerequisites

OCR requires Tesseract to be installed on the host system and available on the PATH. PDF4LLM does not bundle Tesseract.

Windows

Download and run the installer from the UB Mannheim Tesseract builds — the most actively maintained Windows distribution. During installation, select any additional language packs you need. After installation, add the Tesseract directory to your PATH:

C:\Program Files\Tesseract-OCR

Verify the installation:

tesseract --version

macOS

brew install tesseract

# With additional language packs
brew install tesseract-lang

Linux (Debian / Ubuntu)

sudo apt-get install tesseract-ocr

# Add language packs individually
sudo apt-get install tesseract-ocr-fra   # French
sudo apt-get install tesseract-ocr-deu   # German
sudo apt-get install tesseract-ocr-chi-sim  # Simplified Chinese

Verify Tesseract is on the PATH

PDF4LLM calls Tesseract as a subprocess. If Tesseract is installed but not on the PATH, you will get a TesseractNotFoundException at runtime. Confirm it is reachable from the process running your application:

tesseract --version
# Should print: tesseract x.x.x

If running under a service account or in a Docker container, ensure the Tesseract binary is accessible from the application’s environment — not just your interactive shell.

Basic usage

Pass useOcr: true to ToMarkdown, ToText, or ParseDocument:

using MuPDF.NET;
using PDF4LLM;

Document doc = new Document("scanned-report.pdf");

string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true);

doc.Close();

The same flag works across all three extraction methods:

// Markdown
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true);

// Plain text
string text = PdfExtractor.ToText(doc, useOcr: true);

// Structured object model
ParsedDocument parsed = PdfExtractor.ParseDocument(doc, useOcr: true);

OCR output goes through the same layout analysis as native text — reading order, heading detection, table detection — before being converted to your chosen format.

Specifying a language

Tesseract uses language-specific data files to improve recognition accuracy. The default is English ("eng"). Pass a Tesseract language code to ocrLanguage for other languages:

// French
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "fra");

// German
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "deu");

// Japanese
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "jpn");

// Simplified Chinese
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "chi_sim");

// Traditional Chinese
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "chi_tra");

The language data file for each language must be installed on the host system. If the language pack is missing, Tesseract will fall back to English or throw an error depending on the version.

Common Tesseract language codes

Language	Code	Language	Code
English	`eng`	Russian	`rus`
French	`fra`	Arabic	`ara`
German	`deu`	Hindi	`hin`
Spanish	`spa`	Japanese	`jpn`
Italian	`ita`	Korean	`kor`
Portuguese	`por`	Simplified Chinese	`chi_sim`
Dutch	`nld`	Traditional Chinese	`chi_tra`
Polish	`pol`	Turkish	`tur`

The full list of available language codes is maintained in the Tesseract documentation.

Multi-language documents

If a document contains text in more than one language on the same page, pass a +-separated list of language codes:

// English and French mixed on the same page
string markdown = PdfExtractor.ToMarkdown(
    doc,
    useOcr: true,
    ocrLanguage: "eng+fra"
);

Using multiple languages increases recognition time and can reduce accuracy when the languages use very different character sets. Use multi-language mode only when the document genuinely mixes languages on the same page. For documents where different pages use different languages, restrict OCR to each language on its own set of pages using the pages parameter.

Performance

OCR is significantly slower than native text extraction. Tesseract rasterises each page to an image and runs a trained neural network over it — this typically takes 1–5 seconds per page depending on page dimensions, resolution, and hardware, compared to milliseconds for native extraction. For a 200-page scanned document, OCR may take 5–15 minutes. Plan for this in your pipeline.

Process only the pages that need OCR

The most impactful optimisation is to apply OCR only to pages that actually need it. Many documents are partially scanned — a cover page or appendix may be a rasterised image while the body contains selectable text. Identify scanned pages by checking whether native extraction returns useful content:

Document doc          = new Document("mixed-document.pdf");
var      scannedPages = new List<int>();

for (int i = 0; i < doc.PageCount; i++)
{
    // Quick native probe — fast, no OCR
    string native = PdfExtractor.ToText(doc, pages: new List<int> { i });

    // If the page has very little native text, it is likely a scanned image
    if (native.Trim().Length < 50)
        scannedPages.Add(i);
}

// Run OCR only on the pages that need it
string ocrMarkdown = scannedPages.Count > 0
    ? PdfExtractor.ToMarkdown(doc, pages: scannedPages, useOcr: true)
    : string.Empty;

// Run native extraction on the remaining pages
var nativePages = Enumerable.Range(0, doc.PageCount)
    .Except(scannedPages)
    .ToList();

string nativeMarkdown = nativePages.Count > 0
    ? PdfExtractor.ToMarkdown(doc, pages: nativePages)
    : string.Empty;

doc.Close();

Adjust the character threshold (< 50) to your documents. Pages with only a page number or a short chapter title will score low on native extraction — tune conservatively to avoid classifying lightly-populated text pages as scanned.

Process pages in parallel

For large all-scanned documents, parallelise across pages. Each call to ToMarkdown with a single-page pages list is independent:

Document doc       = new Document("large-scanned.pdf");
int      pageCount = doc.PageCount;
var      results   = new string[pageCount];

await Parallel.ForEachAsync(
    Enumerable.Range(0, pageCount),
    new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount },
    async (pageIndex, ct) =>
    {
        results[pageIndex] = PdfExtractor.ToMarkdown(
            doc,
            pages:       new List<int> { pageIndex },
            useOcr:      true,
            ocrLanguage: "eng"
        );
    }
);

doc.Close();

string fullMarkdown = string.Join("\n\n---\n\n", results);

Confirm whether your version of MuPDF.NET supports concurrent access to a shared Document object before using this pattern. If it does not, open a separate Document per task using the file path overload to avoid race conditions.

Show progress for long runs

For documents where OCR will take a noticeable amount of time, enable progress reporting:

string markdown = PdfExtractor.ToMarkdown(
    doc,
    useOcr:       true,
    showProgress: true
);

Mixed documents

A mixed document contains both selectable text pages and scanned image pages. The pattern below produces a single Markdown string in page order, using native extraction where possible and OCR where not:

Document doc           = new Document("mixed.pdf");
var      markdownParts = new List<(int Page, string Content)>();

for (int i = 0; i < doc.PageCount; i++)
{
    string native = PdfExtractor.ToText(doc, pages: new List<int> { i });

    string content = native.Trim().Length >= 50
        ? PdfExtractor.ToMarkdown(doc, pages: new List<int> { i })
        : PdfExtractor.ToMarkdown(doc, pages: new List<int> { i }, useOcr: true);

    markdownParts.Add((i, content));
}

doc.Close();

string fullMarkdown = string.Join(
    "\n\n---\n\n",
    markdownParts.OrderBy(p => p.Page).Select(p => p.Content)
);

This calls ToText once per page as a cheap probe — native extraction is fast — then calls ToMarkdown with the appropriate setting. The total cost is one native-speed pass over every page plus OCR only on the pages that require it.

OCR accuracy

Tesseract accuracy depends heavily on the quality of the input image. Several factors affect results. Resolution — Tesseract is trained on 300 DPI images. Scans below 200 DPI produce noticeably worse results, especially for small or condensed text. If you control the scanning process, scan at 300 DPI minimum. Skew — Pages rotated even a few degrees during scanning significantly reduce accuracy. Most modern scanners de-skew automatically; if yours doesn’t, apply de-skewing pre-processing before extraction. Noise and artefacts — Coffee stains, smudges, fax compression artefacts, and paper grain all reduce accuracy. These cannot be corrected within PDF4LLM. Apply image pre-processing — binarisation, noise removal, contrast enhancement — to extracted page images before passing them to Tesseract if accuracy is critical for your use case. Font type — Tesseract performs best on standard serif and sans-serif fonts. Handwriting, decorative fonts, and highly stylised typefaces are recognised poorly and should not be expected to produce reliable output. Language selection — Using the wrong language model reduces accuracy even for text that looks superficially similar between languages. Always set ocrLanguage to match the document language.

Diagnosing poor accuracy

Use ToText with OCR to inspect raw recognition output without the added complexity of Markdown formatting:

string rawOcrText = PdfExtractor.ToText(
    doc,
    pages:  new List<int> { suspectPageIndex },
    useOcr: true
);
Console.WriteLine(rawOcrText);

Common misrecognition patterns and their likely causes:

What you see	Likely cause
`l` / `1` / `I` confusion	Low resolution or thin font strokes
`0` / `O` confusion	Low resolution or sans-serif font at small size
Missing spaces between words	Page DPI below 200 or high background noise
Garbled non-Latin characters	Wrong `ocrLanguage` or missing language pack
Entire paragraphs absent	Region classified as an image, not a text block
Correct words in wrong order	Multi-column layout not linearised correctly post-OCR

OCR in containerised environments

When running PDF4LLM in Docker or a CI pipeline, Tesseract must be present in the container image. Add it to your Dockerfile:

FROM mcr.microsoft.com/dotnet/runtime:8.0

# Install Tesseract and the English language data pack
RUN apt-get update && apt-get install -y \
    tesseract-ocr \
    tesseract-ocr-eng \
    && rm -rf /var/lib/apt/lists/*

# Add additional language packs as needed
# RUN apt-get install -y tesseract-ocr-fra tesseract-ocr-deu

WORKDIR /app
COPY --from=build /app/publish .

ENTRYPOINT ["dotnet", "MyApp.dll"]

Verify Tesseract is on the PATH inside the built image:

docker run --rm your-image tesseract --version

Tessdata path

Tesseract looks for language data files in the directory specified by the TESSDATA_PREFIX environment variable, or in the default system location (/usr/share/tesseract-ocr/*/tessdata/ on Debian/Ubuntu). If you install language data files to a custom location, set the variable explicitly in your Dockerfile:

ENV TESSDATA_PREFIX=/usr/share/tesseract-ocr/4.00/tessdata/

Troubleshooting

TesseractNotFoundException at runtime Tesseract is not on the PATH for the process running your application. Verify with tesseract --version in the same environment — not just your interactive shell. In Docker, check with docker run --rm your-image tesseract --version. Empty or near-empty output despite useOcr: true The pages may already contain selectable text that is being extracted natively without invoking OCR. Run ToText without useOcr first and check whether content is returned. If OCR is invoked but still returns nothing, the scan DPI is likely very low — check the source image resolution. Garbled or nonsensical text The most common cause is a mismatched ocrLanguage. Confirm the document language and set the correct Tesseract code. For non-Latin scripts (Arabic, CJK, Devanagari), ensure the appropriate language pack is installed and the correct code is used. OCR is extremely slow Processing time scales linearly with page count. Use the page-filtering pattern to restrict OCR to only scanned pages. For bulk pipelines, distribute work across multiple workers rather than processing large documents serially. Tables in scanned documents are not being detected Table detection from OCR output relies on the spatial alignment of recognised character positions, which is less reliable than detecting tables in native PDF text. For scanned documents with critical table data, inspect ToJson output to see how the blocks were classified, and consider building a custom table renderer from ParseDocument for these cases. Language pack missing error from Tesseract Install the required pack for your platform. Debian/Ubuntu: sudo apt-get install tesseract-ocr-{code}. macOS: brew install tesseract-lang. Windows: re-run the Tesseract installer and select the language from the component list.

Next steps

Tables

Table extraction explained.

Page Selection

Process only specific pages to speed up OCR-heavy documents.

Installation

Install Tesseract and the OCR optional dependency.

Images & Graphics

Extract embedded images alongside OCR’d text.

Getting Started

Guides

Integrations

Reference

OCR

Overview

Prerequisites

Windows

macOS

Linux (Debian / Ubuntu)

Verify Tesseract is on the PATH

Basic usage

Specifying a language

Common Tesseract language codes

Multi-language documents

Performance

Process only the pages that need OCR

Process pages in parallel

Show progress for long runs

Mixed documents

OCR accuracy

Diagnosing poor accuracy

OCR in containerised environments

Tessdata path

Troubleshooting

Next steps

Tables

Page Selection

Installation

Images & Graphics

​Overview

​Prerequisites

​Windows

​macOS

​Linux (Debian / Ubuntu)

​Verify Tesseract is on the PATH

​Basic usage

​Specifying a language

​Common Tesseract language codes

​Multi-language documents

​Performance

​Process only the pages that need OCR

​Process pages in parallel

​Show progress for long runs

​Mixed documents

​OCR accuracy

​Diagnosing poor accuracy

​OCR in containerised environments

​Tessdata path

​Troubleshooting

​Next steps

Tables

Page Selection

Installation

Images & Graphics

Overview

Prerequisites

Windows

macOS

Linux (Debian / Ubuntu)

Verify Tesseract is on the PATH

Basic usage

Specifying a language

Common Tesseract language codes

Multi-language documents

Performance

Process only the pages that need OCR

Process pages in parallel

Show progress for long runs

Mixed documents

OCR accuracy

Diagnosing poor accuracy

OCR in containerised environments

Tessdata path

Troubleshooting

Next steps