PDF to TXT Converter

What is Text Extraction from PDF?

Text extraction from PDF is the process of obtaining the textual content of a document in pure form, without formatting, graphics, or structural elements. The result is a TXT file containing only letters, numbers, punctuation marks, and line breaks. Such text can be opened in any editor on any device, used for analysis, indexing, or further processing.

PDF (Portable Document Format) was developed by Adobe in 1993 for exchanging documents while preserving their exact appearance regardless of software and operating system. The format is based on the PostScript page description language and stores information about each element: character coordinates, fonts, colors, images, vector objects. This is why PDF looks the same on computer screens, tablets, phones, and when printed.

TXT (Plain Text) is a simple text format without any formatting. The file contains only a sequence of characters in a specific encoding. TXT appeared at the dawn of the computer era and remains a universal way to store textual information. Text files can be read everywhere: on server command lines, in Windows Notepad, in macOS text editors, on smartphones. File size is minimal — only the characters themselves without metadata.

The PEREFILE service analyzes the PDF document structure, extracts text streams, and creates a text file with proper UTF-8 encoding for correct display of English and other languages. Password-protected documents are supported — simply provide the password during conversion.

How PDF Works Internally

Understanding the internal structure of PDF helps explain why text extraction is a non-trivial task. PDF was designed not for editing, but for accurate reproduction of document appearance.

Streams and Objects

A PDF file is a collection of objects: fonts, images, text streams, graphical elements. Each object has a unique number and can reference other objects. Text is stored not as a sequence of paragraphs, but as a set of drawing commands: "place character X at position Y using font Z."

Example of how the simple word "Hello" might look inside a PDF:

• Set font Arial, size 12
• Move cursor to coordinates (100, 700)
• Draw character "H"
• Move cursor 8 points to the right
• Draw character "e"
• And so on for each character

Encodings and Fonts

Additional complexity is created by the encoding system. In PDF, the same character can have different numeric codes depending on the embedded font. Some documents use font subsets (only characters that appear in the text), and their encodings are unique to each file. The text extraction program must correctly interpret these encodings.

Logical Structure

PDF doesn't necessarily store text in the order it's read. A two-column document might contain all the text from the left column first, then the right. Or mixed — as added during creation. A table might be stored as a set of independent text blocks positioned in cell coordinates. Recovering the logical reading order requires analyzing element positions on the page.

Comparison of PDF and TXT Formats

The formats are designed for diametrically opposite purposes:

PDF is a presentation format, TXT is a pure information storage format. Converting PDF to TXT means extracting content from a beautiful wrapper.

When PDF to TXT Conversion is Needed

Preparing Data for Analysis

Modern text analysis systems work with plain text:

• Machine learning — neural networks are trained on text corpora without formatting. PDF documents require preliminary text extraction
• Sentiment analysis — determining the emotional tone of reviews, comments, publications requires clean text
• Keyword search — automatic identification of document topics
• Document comparison — finding plagiarism, duplicates, changes between versions

For processing an archive of thousands of PDF documents, the first step is mass text extraction into a format accessible for programmatic processing.

Indexing for Search

Corporate document management systems, search engines, archives use text indexes:

• Internal search — find all documents mentioning a specific client or project
• Full-text databases — creating search indexes by document content
• Knowledge management systems — automatic categorization and linking of documents
• Legal and scientific databases — searching court decisions, patents, publications

Text format allows building a fast index without needing to parse the PDF structure each time.

Content Migration

When transferring information between systems, text format acts as a universal intermediary:

• Transfer to website — extracting articles and documents from PDF for CMS publication
• Creating email newsletters — preparing text versions of messages
• Import to databases — loading text content for storage and search
• Conversion to other formats — from TXT it's easy to create Markdown, HTML, Word

Plain text is the lowest common denominator for all content systems.

Automating Document Processing

Scripts and programs work more easily with text files:

• Regex parsing — extracting dates, numbers, email addresses
• Statistics calculation — word count, unique terms, frequency
• Replacement and transformation — mass text processing with sed, awk, Python
• Integration with Unix tools — grep, diff, sort, uniq work directly with text

For automating document processing workflows, TXT is the ideal intermediate format.

Ensuring Accessibility

Text format ensures access to information under any conditions:

• Visually impaired users — screen readers work better with plain text
• Slow connection — text file loads instantly
• Limited devices — old computers, basic phones, e-readers
• Archiving — TXT is guaranteed to open decades from now

When guaranteed readability is important — text format is irreplaceable.

How Text Extraction from PDF Works

The text extraction process includes several stages of intelligent processing.

Stage 1: Document Structure Analysis

The service parses the internal PDF structure:

• Determining number of pages
• Identifying fonts and their encodings
• Detecting text streams
• Determining document protection

If the document is password-protected, the password is requested at this stage for decryption.

Stage 2: Extracting Text Streams

Text data is extracted from each page:

• Decoding font subsets
• Converting internal codes to Unicode characters
• Extracting coordinates of each character
• Preserving information about spaces and line breaks

Stage 3: Recovering Logical Order

Characters are arranged into a readable sequence:

• Grouping characters into words by coordinates
• Combining words into lines
• Determining line order (top to bottom, left to right)
• Processing multi-column layouts
• Recognizing paragraphs and headings

Stage 4: Creating the Text File

The finished text is saved with proper encoding:

• UTF-8 encoding for support of all languages
• Universal line breaks
• Preserving paragraph structure
• File available for download

Conversion Features

What is Preserved in TXT

• All document text — main content is fully transferred
• Page order — text is extracted sequentially from all pages
• Paragraphs and line breaks — text structure is preserved where possible
• Table content — text from cells is extracted
• List numbering — numbers and bullets are preserved as text
• Footnotes and notes — if they are textual

What is Lost in Conversion

• Fonts and sizes — all characters become equivalent
• Bold, italic, underline — highlighting is not transferred
• Text and background colors — TXT doesn't support colors
• Images and graphics — not included in the text file
• Table structure — cell borders and alignment are lost
• Hyperlinks — only visible text remains, URL is lost
• Headers and footers — top and bottom page margins
• Page numbering — relates to visual presentation
• Forms and interactive elements — not transferred
• Annotations and comments — not included

Difference from Text Recognition (OCR)

It's important to understand the difference between text extraction and OCR:

Text Extraction (PDF → TXT)

Works with documents where text is stored digitally:

• PDF created from a text editor (Word, LaTeX, Google Docs)
• PDF generated by a program (invoices, reports, receipts)
• Text can be selected and copied in the PDF viewer

Extraction is fast and accurate — text is simply read from the file.

Text Recognition (OCR)

Works with images where text needs to be "seen":

• Scanned paper documents
• Page photographs
• PDFs where pages are images

OCR analyzes pixels, identifies characters, may make errors.

How to determine your PDF type:

1. Open the document in any PDF viewer
2. Try to select text with the mouse
3. If text is selectable — it's a text PDF, use regular conversion
4. If text is not selectable — it's a scanned document, OCR is needed

PEREFILE provides both tools: PDF to TXT conversion for text documents and OCR for scanned ones.

Working with Protected PDFs

PDF documents are often protected to restrict access or actions.

Types of Protection

1. Open password (user password) — document is encrypted, content cannot be viewed without password
2. Permissions password (owner password) — document opens, but actions are restricted: printing, copying, editing

Converting Protected Documents

For documents with an open password, you need to provide the password when uploading. The service will decrypt the content and extract the text.

Documents protected only with permissions usually convert without issues — protection restricts user actions in viewing programs but doesn't encrypt the content.

If the password is unknown, conversion of a protected document is impossible.