Bulk Transliteration — Convert Large Texts and Files Between Scripts

The standard transliteration flow — open editor, type a sentence, see the converted text — works well for short to medium length content. But when you have a 10,000-word document, a spreadsheet with 500 product names, or a database dump of 2,000 addresses that need to appear in Hindi, typing them one by one is not a workflow. That's what the bulk tool at TranslitHub is for.

This guide covers the bulk tool's capabilities, the different input formats it accepts, how to handle CSV and spreadsheet data, and practical advice for getting clean output at scale.

What Bulk Transliteration Actually Does

The single-entry editor converts phonetic Roman text to an Indian script as you type, word by word. The bulk tool does the same conversion but on a file or large text block in one go — you submit everything at once and get the converted output back, ready to download.

There are two modes:

Text block mode: Paste up to 50,000 characters of Roman text into a large textarea, set the target language, and click Convert. The output appears in a second panel, which you can then edit or download. File upload mode: Upload a TXT, CSV, or DOCX file. For plain text files, the entire content is converted. For CSV files, you specify which columns to convert and which to leave as-is. The processed file downloads with converted text in place.

Supported Input Formats

Format	Max Size	Notes
Paste (text block)	50,000 characters	Direct input, no file needed
TXT	5MB	UTF-8 encoded; converts entire content
CSV	10MB	Selective column conversion
DOCX	10MB	Converts text content; preserves formatting
XLSX	10MB	Like CSV but for Excel files

For files larger than the limits above, use the API (which has higher limits and is designed for bulk processing programmatically) or split the file into smaller chunks.

Converting a Text File

The simplest use case: you have a TXT file with content in Roman phonetic spelling and want a version in Devanagari, Tamil, or another script.

1. Go to the Bulk Transliteration tool on TranslitHub
2. Upload your TXT file
3. Select the target language
4. Set any conversion options (numbers, English words, etc.)
5. Click Process
6. Download the converted TXT file

The output filename appends the language code: content.txt becomes content_hi.txt.

What About Mixed Content?

If your file contains both English text you want to keep as English and phonetic Indian language text you want to convert, use the [TRANSLIT]...[/TRANSLIT] markup:

Our company mission is to [TRANSLIT]duniya bhar mein logon ki madad karna[/TRANSLIT] through technology.

Only the content between the markers gets converted. Everything outside stays in English. The markers are stripped from the output:

Our company mission is to दुनिया भर में लोगों की मदद करना through technology.

This is particularly useful for bilingual content — English documents with Hindi/regional language sections.

CSV and Spreadsheet Processing

This is where bulk transliteration becomes genuinely powerful for business use cases.

Basic CSV Workflow

Suppose you have a product catalog with English product names that need Hindi transliterations added:

Input CSV:

product_id,name_en,price,category
P001,namkeen-packets,45,snacks
P002,chai-masala,120,spices
P003,ghee-dabba,380,dairy

1. Upload the CSV
2. In the column mapping dialog, mark name_en as "Convert" and all other columns as "Keep as-is"
3. Set the target language to Hindi
4. Set the output column name to name_hi
5. Click Process

Output CSV:

product_id,name_en,name_hi,price,category
P001,namkeen-packets,नमकीन पैकेट्स,45,snacks
P002,chai-masala,चाय मसाला,120,spices
P003,ghee-dabba,घी डब्बा,380,dairy

The original columns are untouched. The new Hindi column is inserted next to the source column.

Multiple Target Languages

For multilingual catalogs or applications, you can convert a single column to multiple Indian languages in one pass:

1. Select the source column
2. Check multiple target languages (e.g., Hindi, Tamil, Telugu)
3. The output will have separate columns for each: name_hi, name_ta, name_te

This is significantly faster than running the tool three separate times.

Column Headers and Character Encoding

Bulk tool handles CSV files with and without headers. When headers are present, you map columns by name. Without headers, you map by column number (Column 1, Column 2, etc.).

Input CSVs must be UTF-8 encoded. If your spreadsheet was exported from Excel on Windows, it might be in CP1252 encoding — this will cause character issues for any accented or special characters in the file. Export from Excel as "CSV UTF-8" to avoid this.

XLSX (Excel) Processing

The XLSX mode works identically to CSV but preserves the Excel format — cell types, column widths, row colors, and other spreadsheet metadata. The converted columns are inserted adjacent to the source columns.

Formula cells are not converted — only cells containing plain text values. If a cell contains a formula like =A1&" retail", the formula is preserved and the cell is not touched.

Note on Hindi/regional language display in Excel: Excel on Windows renders Indian scripts correctly as long as you have a compatible font installed (Mangal is bundled with Windows). Excel on older macOS versions sometimes has poor rendering for complex scripts — if this affects you, the CSV → edit in Google Sheets → re-export route tends to work better.

Large Document Transliteration

For DOCX files (Word documents):

• Text in paragraphs, headings, tables, and text boxes is converted
• Formatting (bold, italic, font size, color) is preserved
• Images and shapes are untouched
• The document structure (sections, page breaks, headers/footers) is maintained
• Page headers and footers are converted alongside the main body

A 10,000-word DOCX typically processes in 30-60 seconds. For very large documents (80,000+ words), processing may take a few minutes.

Performance Tips for Large Files

Break up very large files: Files approaching the size limit process more slowly and are more likely to time out if your connection is interrupted. Files under 2MB are much faster. Remove unnecessary content first: Before uploading a DOCX for bulk conversion, delete images, charts, and embedded objects. The tool processes only text, so non-text content just adds file size without adding useful content. Use CSV for database content: If you're converting database records, export to CSV rather than DOCX or TXT. The column structure makes it easier to verify the output and reimport the data. Check a sample first: Before processing a 500-row CSV, paste the first 20 rows as a text block and manually verify the quality of conversion. This lets you adjust settings (like whether to preserve certain words in English) before committing the full file.

Handling Numbers in Bulk Data

The preserveNumbers option defaults to true — Arabic numerals (0-9) stay as Arabic numerals in the output. This is the right default for most use cases: product codes, prices, phone numbers, ZIP codes, and similar data should not be converted to native script numerals.

Turn it off only when you specifically want native script numerals — for example, a formal Sanskrit or traditional Hindi document where Devanagari numerals (०-९) are expected.

Handling Proper Nouns

Bulk transliteration has a challenge with proper nouns: the name "Sharma" should probably become "शर्मा" in Hindi conversion, but "Samsung" should probably stay "Samsung" — it's a brand name, not a Hindi word.

TranslitHub handles this with a curated list of known brand names, place names, and commonly encountered proper nouns that are left unconverted. For names not on this list, there's a proper nouns file you can upload alongside your main file — a simple two-column CSV mapping the English name to its intended output. The bulk tool uses this as an override list during conversion.

For example, a proper nouns override file:

English,Transliterated
Samsung,सैमसंग
Maruti,मारुति
Chennai,चेन्नई

Reviewing Bulk Output

Don't treat bulk output as final without review. Especially for:

• Ambiguous words where multiple transliterations are valid (Hindi "baath" vs "baat" — बाथ vs बात)
• Context-dependent words that take different Hindi equivalents depending on meaning
• Technical terms that may not be in the conversion model's vocabulary

For quality assurance on important content (marketing copy, official documents), review the output line by line. For bulk data where a few errors are tolerable (category names in a product catalog), spot-checking a random sample is usually sufficient.

Common Use Cases

E-commerce catalogs: Product names and descriptions need to appear in Hindi, Tamil, and other regional languages for regional storefronts. Bulk convert the English column, review, and upload. Government records digitization: Form data, citizen records, address databases in Roman transliteration need to be converted to native scripts for the official record. Educational content: English-written notes and worksheets for students learning through a regional language medium need to be converted for distribution. Publishing: Publishers translating books or textbooks need bulk conversion of properly transliterated manuscripts. Mobile app localization: App strings in Roman transliteration need to be converted to correct scripts for Indian language app versions.

Related Tools

• Transliteration API — programmatic bulk processing for developers
• Document Export — export your converted documents as PDF or Word
• OCR for Indian Scripts — extract text from images before bulk conversion