Extract Text From Tool

Extracting text from a marked-up or structured source means stripping every formatting code, tag, and data delimiter until only the underlying words remain. Seven input formats are supported: HTML, BBCode, JSON, CSV, XML, Markdown, and YAML, and an auto-detect mode identifies which one it is facing without the format being named explicitly.

Auto-detection looks for signature patterns rather than a file extension: angle brackets identify HTML and XML, square-bracket tags identify BBCode, an opening curly brace or square bracket identifies JSON, front-matter fences identify YAML, hash-prefixed headings identify Markdown, and comma-separated values identify CSV.

Each parser targets the structural elements unique to its format. The HTML and XML parsers strip every tag and can decode character entities back to their literal characters. The BBCode parser removes bracket-delimited tags, including nested constructs such as [url=...]...[/url] and [color=...]...[/color]. The JSON parser walks the data tree recursively and collects every string value, skipping keys, numbers, booleans, and null entries. The CSV parser splits rows by newlines and cells by commas, then joins the cell values into readable text. The Markdown parser removes heading markers, emphasis markers, link and image syntax, code fences, blockquote prefixes, and horizontal rules. The YAML parser extracts scalar string values from key-value pairs and list items, ignoring the structural indicators, colons, dashes, and indentation, that carry no text of their own.

The extracted output is reported alongside character, word, and line counts, so a change in length is visible immediately. Line breaks from the source can be preserved or collapsed, runs of spaces and tabs can be trimmed to a single space, and HTML character references such as &, <, and ' can be decoded back to the characters they represent.

Extracting plain text from formatted sources is a frequent step in content migration, data cleaning, analysis pipelines, and accessibility workflows. The scenarios below illustrate specific situations where removing markup or data formatting produces immediately useful output.

• Content migration: Move forum posts written in BBCode to a platform that accepts only plain text or a different markup language. Strip the BBCode tags first, then reformat as needed for the destination system.
• Web scraping cleanup: After copying content from a web page, remove residual HTML tags, inline styles, and entity references to obtain a clean text version suitable for spreadsheets or documents.
• API response inspection: Extract only the human-readable string values from a JSON API response, filtering out keys, numeric identifiers, and boolean flags that clutter manual review.
• CSV data review: Import a CSV export from a database or analytics platform and flatten it into readable text to scan the content without opening a spreadsheet application.
• Documentation conversion: Strip Markdown formatting from README files, wiki pages, or technical documentation to produce plain text copies for email distribution or plain-text archives.
• Configuration auditing: Extract the human-readable values from YAML configuration files to review settings in a flat text format without the structural indentation and key hierarchy.
• XML feed processing: Pull the textual content from RSS feeds, SOAP responses, or XML data exports, discarding the element tags and namespace declarations that surround each value.
• Accessibility testing: Verify that the visible text content of an HTML page reads coherently when all markup is removed, simulating how a text-only browser presents the content.