Transliteration is the process of converting texts from one script to another based on the phonetic similarity. Here, the text is displayed in alphabets of different languages, but the pronunciation, grammar, and sense of the original version remain intact in these new characters. The transliteration is involved while converting the names, addresses, titles, and more into Indian languages as localization is important without changing meaning.
For better understanding, refer to the below examples:
Example:
Type | English | Hindi |
---|---|---|
City Name | New Delhi | न्यू दिल्ली |
City Name | Pune | पुणे |
Address | Evershine Park, Veera Desai Industrial Estate, Andheri West, Mumbai, Maharashtra 400102 | एवरशाइन पार्क, वीरा देसाई इंडस्ट्रियल इस्टेट, अन्धेरी वेस्ट, मुंबई, महाराष्ट्र 400102 |
Address | Total Mall, Behind Koramangala, Bengaluru | टोटल मॉल, बिहाइंड कोरमंगला, बेंगलुरु |
Reverie’s Transliteration comes with unique features that have been tuned over years of working with Industry and Academia
Reverie’s Transliteration for Travel has domain specific abbreviations in-built.
Abbreviation | Source | Transliterated |
---|---|---|
rd | mahatma gandhi rd | महात्मा गांधी रोड |
st | bakers st | बेकर्स स्ट्रीट |
It also intelligently understands whether the input word is an abbreviation or not. It tries to predict if the input word passed in CAPITAL letters needs to be converted into abbreviation or should be treated as a normal word, also does the same for input words typed in lower case.
Source | Transliterated |
---|---|
YEQ Infra Mumbai | वाई.ई.क्यू. इन्फ्रा मुंबई |
PEEL orange | पील ऑरेंज |
asset twq kalyan nagar | ऐसेट टी.डब्ल्यू.क्यू. कल्याण नगर |
Reverie’s Transliteration successfully identifies the context of a word used in the source content and then transliterates accordingly. This feature will decrease the editorial work as it can recognize the meaning of the word and help the native language users consume the content in the right order.
Source Content | Transliterated Content |
---|---|
1st main, 3rd st, st. johns road | फर्स्ट मेन, थर्ड स्ट्रीट, सेंट. जॉन्स रोड |
In the above example, the word st appears thrice:
Suffix for 1 (1st)
Acronym for Street
Acronym for Saint
The intelligent solution will automatically analyze the statement’s context and transliterate as First, 3rd street, and Saint appropriately.
Language | Language ISO Code |
---|---|
hindi | hi |
marathi | mr |
punjabi | pa |
gujarati | gu |
tamil | ta |
telugu | te |
malayalam | ml |
kannada | kn |
odia | or |
assamese | as |
bengali | bn |
Element | Type | Is Mandatory? | Description | ||||||
---|---|---|---|---|---|---|---|---|---|
src_lang |
string | Yes | Source script to convert from. | ||||||
tgt_lang |
string | Yes | Target script to convert to. | ||||||
domain |
number | No | While domain parameter is optional, it gives better accuracy of conversion when you know what is the content that you are willing to convert. This is important as different proper nouns have their own nuances. This is an optional parameter
|
Element | Type | Is Mandatory? | Description | ||||
---|---|---|---|---|---|---|---|
data |
array of content | Yes | List of input text for transliteration. | ||||
convertNumber |
boolean | No | Specify whether to convert the numbers in the input text to the target language script. Note: By default, the convertNumber value is false Example:
|
{
"data": [
"Evershine Park, Veera Desai Industrial Estate, Andheri West, Mumbai, Maharashtra 400102"
],
"convertNumber": true
}
{
"responseList": [
{
"apiStatus": 2,
"inString": "Evershine Park, Veera Desai Industrial Estate, Andheri West, Mumbai, Maharashtra 400102",
"outString": [
"एवरशाइन पार्क, वीरा देसाई इंडस्ट्रियल इस्टेट, अन्धेरी वेस्ट, मुंबई, महाराष्ट्र ४००१०२"
]
}
]
}