## **About** 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, song titles, movie names, and more into Indian languages as localization is important without changing meaning. For better understanding, refer to the below examples: Example: | Type | English | Hindi | | --------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | Movie Name |Mission Mangal | मिशन मंगल | | Song Title | Dil mein mars hai | दिल में मार्स है | | Artist Name | A R Rahman | ए आर रहमान | ## **Features** Reverie's Transliteration comes with unique features that have been tuned over years of working with Industry and Academia. ## Abbreviations Reverie's Transliteration for Media has domain specific abbreviations in-built. Abbreviation|Source |Transliterated ------------|-----------------|----------------- mr |mr India |मिस्टर इण्डिया st |bakers st |बेकर्स स्ट्रीट dr |dr kotnis ki amar kahani |डॉ. कोटनिस की अमर कहानी 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 ----------------------|-------------------------------- YEQ Infra Mumbai |वाई.ई.क्यू. इन्फ्रा मुंबई PEEL orange|पील ऑरेंज asset twq kalyan nagar|ऐसेट टी.डब्ल्यू.क्यू. कल्याण नगर URI: The Surgical Strike |उरी: द सर्जिकल स्ट्राइक ## Disambiguation 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. **Example 2** Source Content |Transliterated Content --------------------------------|----------------------------------------- Singh Sahab the great, aap kahan the | सिंह साहब द ग्रेट, आप कहां थे In the above example, the source content is written in English script (Latin Script), it consists of words of both English and Hindi languages. However, the word "THE" is used twice in the sentence: as an adjective and once in the Hindi meaning “where were you?”. Our API has successfully recognized the context and transliterated it accordingly. **Example 3** Source Content |Transliterated Content --------------------------------|----------------------------------------- dil me ho tum | दिल मे हो तुम love me like you do | लव मि लाइक यू डू Here the word 'me' is an ambiguous word. For the first sentence, it's giving as मे and for the second sentence, it's मि. This is the unique feature of our API, where it can analyse the sentence of different languages and gives result accordingly. ## **Languages** 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 ## **API Details** ### Header Details 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

Content Type	Domain
Music	25
Names	2

### Body Details Element |Type |Is Mandatory?|Description --------------------|----------------|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- `data` |array of content|Yes |List of input text for transliteration. ## **Sample Request & Response** **Sample 1** ## Request ``` { "data": [ "Mera Dil Bhi Kitna Pagal Hai" ] } ``` ## Response ``` { "responseList": [ { "apiStatus": 2, "inString": "Mera Dil Bhi Kitna Pagal Hai", "outString": [ "मेरा दिल भी कितना पागल है " ] } ] } ``` **Sample 2** ## Request ``` { "data": [ "Let me love you" ] } ``` ## Response ``` { "responseList": [ { "apiStatus": 2, "inString": "Let me love you", "outString": [ "लेट मि लव यू " ] } ] } ```