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Smart Text Case ✨

Smart Text Case is an AI-powered capitalization tool that understands context. Unlike rules-based transformation, it knows when “ceo” is an acronym and “cat” is not, handles complex name patterns like Mc/Mac and O’ prefixes, and fixes accidental caps lock input intelligently.



  1. Open the tool ExtensionsText To Table ConverterMega ToolsSmart Text Case.

    You can also access this from the Text Case Transform panel by switching to the AI Smart Fix ✨ tab.

  2. Select your data Select the cells containing text you want to fix. This tool works on the current selection.

  3. Choose data context Select what kind of data you’re working with from the dropdown or chips:

    • Auto-Detect (default) - AI guesses the best format based on content analysis
    • People Names - Handles complex names correctly (e.g., “leonardo dicaprio” → “Leonardo DiCaprio”, “sean o’malley” → “Sean O’Malley”, “ronald mcdonald” → “Ronald McDonald”)
    • Addresses - Standardizes street types and directional acronyms (e.g., “123 n. main st, apt 4” → “123 N. Main St, Apt 4”)
    • Product Titles - Optimized for e-commerce, capitalizing major words while keeping measurements lowercase (e.g., “iphone 13 pro max 256gb” → “iPhone 13 Pro Max 256GB”)
    • Sloppy User Input - Fixes text typed with accidental caps lock or mixed case (e.g., “tHANKS fOR yOUR hELP” → “Thanks for your help”)
  4. Analyze & Preview Click Analyze & Preview to process your data with AI.

    You’ll see a progress bar: “Analyzing 50 rows with AI…” - AI processing takes longer than standard rules.

  5. Review the diff preview The results window shows a detailed side-by-side comparison:

    • Checkbox - Select which changes to apply
    • Location - Cell reference (e.g., Sheet1!A5)
    • Current Value - Your original text
    • New Value (Preview) - AI-suggested capitalization
    • Highlighted Changes - Specific characters that changed are highlighted in green/red

    Example: “john macdonald” → “John MacDonald” (AI recognized the Scottish prefix)

  6. Apply Changes After reviewing the preview, click Apply Changes to update the selected cells.


Let the AI analyze your content and choose the best capitalization approach. Works well for mixed data types.

Best for: General cleanup, mixed content, when you’re unsure

Handles complex name patterns that standard rules miss:

  • Scottish/Irish prefixes: MacDonald, O’Connor, McCarthy
  • Capitalization: DiCaprio, von Neumann, de la Cruz
  • Compound names: Jean-Pierre, Mary Anne
  • Titles: Dr. Smith, Ms. Johnson

Examples:

  • “leonardo dicaprio” → “Leonardo DiCaprio”
  • “sean o’malley” → “Sean O’Malley”
  • “ronald mcdonald” → “Ronald McDonald”
  • “sarah o’connor” → “Sarah O’Connor”

Standardizes street addresses with proper abbreviations and formatting:

  • Street types: St, Ave, Blvd, Rd, Dr
  • Directions: N, S, E, W, NE, SW
  • Unit types: Apt, Suite, Unit, Floor
  • Building numbers: Preserves numbers correctly

Examples:

  • “123 n. main st, apt 4” → “123 N. Main St, Apt 4”
  • “456 elm avenue suite 200” → “456 Elm Avenue Suite 200”

Optimized for e-commerce and product catalogs:

  • Brand names: iPhone, iPad, PlayStation, Xbox
  • Model numbers: Preserves numbers and variants
  • Measurements: Keeps units lowercase when appropriate
  • Product attributes: Capitalizes colors, sizes, materials

Examples:

  • “iphone 13 pro max 256gb” → “iPhone 13 Pro Max 256GB”
  • “samsung galaxy s23 ultra black” → “Samsung Galaxy S23 Ultra Black”
  • “macbook pro 14 inch m2” → “MacBook Pro 14 Inch M2”

Fixes text typed with accidental caps lock or random mixed case:

  • Caps lock mistakes: All caps or inverted case
  • Random capitalization: Inconsistent mixed case
  • Intent correction: Understands what the user meant

Examples:

  • “tHANKS fOR yOUR hELP” → “Thanks for your help”
  • “PLEASE SEND THE REPORT” → “Please send the report”
  • “hELLO wORLD” → “Hello world”

Input CellRules-Based “Title Case”AI Smart Fix (Context-Aware)
sarah o’connorSarah O’connor ❌Sarah O’Connor ✅
ceo of ibmCeo Of Ibm ❌CEO of IBM ✅
iphone 14 proIphone 14 Pro ❌iPhone 14 Pro ✅
100mg vitamin c100Mg Vitamin C ❌100mg Vitamin C ✅
tHANKS fOR yOUR hELPTHANKS FOR YOUR HELP ❌Thanks for your help ✅

The AI naturally knows “CEO” is an acronym and “cat” is not - no need to maintain exception lists.

Handles Mc/Mac prefixes, O’ patterns, and other name conventions that would require complex rules.

Fixes accidental caps lock by understanding what the user meant to type.

Knows that “st” means “Street” in addresses but “St” means “Saint” in names.

Recognizes “iPhone”, “eBay”, “LinkedIn”, “PayPal” and preserves their specific capitalization.


  • Contact lists - Fix imported names with complex patterns (O’Brien, MacDonald, DiCaprio)
  • Product catalogs - Standardize product titles from various suppliers
  • Address cleanup - Normalize address formatting from user input or imports
  • Survey data - Fix responses typed with accidental caps lock
  • E-commerce - Ensure product names match brand guidelines (iPhone, not Iphone)
  • CRM data - Clean up messy customer names and addresses from multiple sources
  • Data migration - Fix capitalization issues when importing from legacy systems

  • Processing speed: AI processing is slower than rules-based transformation
  • Best practices: Start with smaller selections (< 100 cells) to test, then scale up
  • Preview first: Always use “Analyze & Preview” for large datasets to verify results
  • Batch processing: For very large datasets, process in batches of 500-1000 cells

If AI processing fails:

  • You’ll see a user-friendly error message
  • Your original data remains unchanged
  • Try reducing the selection size
  • Check your internet connection
  • Consider using rules-based transformation as a fallback

  • You have complex names with Mc/Mac, O’, or other special prefixes
  • Working with brand names that need specific capitalization
  • Cleaning up messy user input with random caps lock
  • You want context-aware capitalization without maintaining exception lists
  • Data includes acronyms mixed with regular words
  • You need fast, deterministic transformations
  • Working with simple data that follows standard rules
  • You want predictable, repeatable results
  • Processing very large datasets (10,000+ cells)
  • You have a specific list of exceptions to preserve