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Text Inspector, Case Converter & Visual Diff Checker

Convert text cases (camelCase, kebab-case, snake_case, PascalCase, UPPERCASE), analyze word/character metrics and reading times, and visually compare text diffs.

Universal case conversion: camelCase, PascalCase, snake_case, kebab-case, CONSTANT_CASE, and URL slugs
In-depth text metrics: word count, character count (with/without spaces), sentence count, and reading time
Side-by-side and inline visual text diff comparison with character-level change highlighting
Text cleanup utilities: remove duplicate lines, sort lines alphabetically, strip whitespace, and trim
Instant copy and clean text export with zero server upload
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Comprehensive Technical Manual

The Text Transformation & String Manipulation Reference Guide

In-depth specifications, architectural mechanics, real-world code implementations, and industry best practices.

01

Case Naming Conventions Across Programming Ecosystems

  • • Different programming languages and runtime frameworks adhere to strict identifier casing conventions:
  • • camelCase: JavaScript/TypeScript variables, functions, and object properties (e.g., userProfileData).
  • • PascalCase: React/Vue components, C# classes, and TypeScript type names (e.g., UserProfileCard).
  • • snake_case: Python variables, PostgreSQL column names, and Rust identifiers (e.g., user_profile_data).
  • • kebab-case: CSS class names, HTML attributes, and REST URL slugs (e.g., user-profile-data).
  • • CONSTANT_CASE: Environment variables and global constants (e.g., MAX_RETRY_COUNT).
Implementation Example
// Universal Casing Transformation Functions in TypeScript
export function toCamelCase(str: string): string {
  return str.toLowerCase().replace(/[^a-zA-Z0-9]+(.)/g, (_, chr) => chr.toUpperCase());
}

export function toSnakeCase(str: string): string {
  return str.replace(/([a-z])([A-Z])/g, '$1_$2').replace(/[^a-zA-Z0-9]+/g, '_').toLowerCase();
}

export function toKebabCase(str: string): string {
  return str.replace(/([a-z])([A-Z])/g, '$1-$2').replace(/[^a-zA-Z0-9]+/g, '-').toLowerCase();
}
02

Text Analytics: Handling Emojis and Unicode Grapheme Clusters

Standard JavaScript string.length counts UTF-16 code units rather than visual characters (glyphs). An emoji like "👨‍👩‍👧‍👦" consists of multiple Unicode code points joined by zero-width joiners (ZWJ), yielding a string.length of 11. Modern text analytics utilize the Intl.Segmenter API to accurately count human-perceived characters (grapheme clusters) and words across all international languages.

Implementation Example
// Accurate Grapheme Cluster & Word Counting via Intl.Segmenter
export function countAccurateGraphemes(text: string): number {
  const segmenter = new Intl.Segmenter('en', { granularity: 'grapheme' });
  return Array.from(segmenter.segment(text)).length;
}

export function countAccurateWords(text: string): number {
  const segmenter = new Intl.Segmenter('en', { granularity: 'word' });
  return Array.from(segmenter.segment(text)).filter((s) => s.isWordLike).length;
}
03

Visual Text Diffing: The Myers Diff Algorithm

Text comparison engines compute differences between two documents using the Myers Diff algorithm (Longest Common Subsequence - LCS). The algorithm calculates the minimum edit script required to transform Document A into Document B, isolating inserted, deleted, and unchanged character spans.

Implementation Example
// Levenshtein Distance (Edit Distance) in TypeScript
export function levenshteinDistance(a: string, b: string): number {
  const matrix = Array.from({ length: a.length + 1 }, (_, i) => [i]);
  for (let j = 1; j <= b.length; j++) matrix[0][j] = j;
  for (let i = 1; i <= a.length; i++) {
    for (let j = 1; j <= b.length; j++) {
      const cost = a[i - 1] === b[j - 1] ? 0 : 1;
      matrix[i][j] = Math.min(
        matrix[i - 1][j] + 1,
        matrix[i][j - 1] + 1,
        matrix[i - 1][j - 1] + cost
      );
    }
  }
  return matrix[a.length][b.length];
}
04

URL Slug Generation and Diacritic Normalization

Converting human titles into SEO-friendly URL slugs requires normalizing accented Unicode diacritics (e.g., "é" -> "e", "ü" -> "u") via String.prototype.normalize('NFD'), stripping non-alphanumeric characters, and collapsing whitespace into single hyphens.

Implementation Example
export function slugify(text: string): string {
  return text
    .normalize('NFD')                   // Decompose accented characters
    .replace(/[\u0300-\u036f]/g, '')   // Strip diacritic marks
    .toLowerCase()
    .trim()
    .replace(/[^a-z0-9\s-]/g, '')       // Remove invalid characters
    .replace(/[\s_-]+/g, '-')           // Collapse spaces to hyphens
    .replace(/^-+|-+$/g, '');           // Trim edge hyphens
}
Knowledge Base & Clarifications

Frequently Asked Questions: Text Utilities

Got questions about how Text Utilities operates, client-side cryptographic safety, or performance limits? Explore common answers below.

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