Transliteration is the process of converting texts from one script to another based on 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. Transliteration is involved while converting the names, addresses, bank names, banking terminologies and more into Indian languages as localization is important without changing meaning.
For better understanding, refer to the below examples:
Example:
Type | English | Hindi |
---|---|---|
Bank Name | ICICI Bank Ltd | आई.सी.आई.सी.आई बैंक लिमिटेड |
Banking Terminology | Market Liability Ratio | मार्केट लायबिलिटी रेशियो |
Address | Nawab Ganj, Azad Market, Delhi | नवाब गंज, आजाद मार्केट, दिल्ली |
Reverie’s Transliteration comes with unique features that have been tuned over years of working with Industry and Academia.
Reverie’s Transliteration for Banking has domain specific abbreviations in-built.
Abbreviation | Source | Transliterated |
---|---|---|
dr | dr amount | डेबिट अमाउंट |
hrs | 24 hours | 24 आवर्स |
ltd | HSBC ltd | एच.एस.बी.सी. लिमिटेड |
coop | Bangalore City Coop Bank | बैंगलोर सिटी कोऑपरेटिव बैंक |
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 an abbreviation or should be treated as a normal word, also does the same for input words typed in lower case.
Source | Transliterated |
---|---|
ING Vysya Bank | आई.एन.जी. वैश्य बैंक |
CAMELS rating system | कॅमल्स रेटिंग सिस्टम |
asset twq kalyan nagar | ऐसेट टी.डब्ल्यू.क्यू. कल्याण नगर |
NABARD | नाबार्ड |
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.
Example 1
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 it 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:
|
Sample 1
{
"data": [
"Reserve Bank of India"
]
}
{
"responseList": [
{
"apiStatus": 2,
"inString": "Reserve Bank of India",
"outString": [
"रिज़र्व बैंक ऑफ इंडिया"
]
}
]
}
Sample 2
{
"data": [
"National Electronic Fund Transfer (NEFT)"
]
}
{
"responseList": [
{
"apiStatus": 2,
"inString": "National Electronic Fund Transfer (NEFT)",
"outString": [
"नैशनल इलेक्ट्रॉनिक फंड ट्रांसफ़र (एन.ई.एफ.टी.)"
]
}
]
}