[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-153775-105":59,"doc-detail-153775-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","language-detection-for-transliterated-content-bert-based-approach","Language Detection for Transliterated Content - BERT-Based Approach","","The research addresses language identification for transliterated text typed in the English alphabet, a common texting behavior that blurs linguistic signals and creates a key challenge for language technology. A dataset of phone text messages in Hindi and Russian transliterated into English is used. The study applies BERT for language classification and a Google Translate API-based conversion pipeline to map transliterated output back to native scripts. Validation accuracy reaches 99%, supporting robust performance and enabling downstream uses such as moderation and analytics.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/language-detection-for-transliterated-content-bert-based-approach/153775/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/language-detection-for-transliterated-content-bert-based-approach/153775.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is language identification difficult for transliterated texting?","Question",{"text":112,"@type":113},"Because users write native-language messages using the English alphabet, the surface form no longer directly reflects the original language, making detection challenging for language technology.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What dataset and languages are used in the study?",{"text":117,"@type":113},"The dataset contains phone text messages with transliterated Hindi and Russian written in English characters.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed system identify and convert transliterated text?",{"text":121,"@type":113},"It uses BERT to classify the transliterated input into the correct language label, and it employs a Google Translate API workflow to convert transliterated Hindi/Russian back into standard native scripts.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},153775,1787882189,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":52,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","Language Detection for Transliterated Content  \nDr. Selva Kumar S  \nDepartment of Computer Science and Engineering  \nB. M. S. College of Engineering Bangalore, India[selva.cse@bmsce.ac.in](selva.cse@bmsce.ac.in)  \nAfifah Khan Mohammed Ajmal Khan Department of Computer Science and Engineering  \nB. M. S. College of Engineering Bangalore, India [afifah.cs20@bmsce.ac.in](afifah.cs20@bmsce.ac.in)  \narXiv :2401 .046 19v 1 [ cs .CL] 9 Jan 2024  \nChirag Manjeshwar Department of Computer Science and Engineering  \nB. M. S. College of Engineering Bangalore, India [chirag.cs20@bmsce.ac.in](chirag.cs20@bmsce.ac.in)  \nImadh Ajaz Banday Department of Computer Science and Engineering  \nB. M. S. College of Engineering Bangalore, India [imadh.cs20@bmsce.ac.in](imadh.cs20@bmsce.ac.in)  \nAbstract—In the contemporary digital era, the Internet functions as an unparalleled catalyst, dismantling geographical and linguistic barriers, particularly evident in texting. This evolution facilitates global communication, transcending physical distancesand fostering dynamic cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is employed to convey messages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text messages in Hindi, and Russian transliterated into English, utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication. Emphasizing the pivotal role of comprehensive datasets for training Large Language Models (LLMs) like BERT, our model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text. With a validation accuracy of 99%, our model’s robust performance underscores its reliability. The comprehensive exploration of transliteration dynamics, supported by innovative approaches and cutting-edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital communication. Beyond contributing to language identification and transliteration capabilities, this work holds promise for applications in content moderation, analytics, and fostering a globally connected community engaged in meaningful dialogue.  \nIndex Terms—Transliteration, Language Identification, Digital Communication, Large Language Models (LLMs), BERT Architecture, Internet Communication.  \nI. INTRODUCTION  \nIn the contemporary digital landscape, the Internet acts as an unparalleled facilitator, dismantling both geographical and linguistic barriers. This transformation is particularly evident in the realm of texting, where individuals from varied language backgrounds converge to communicate and effortlessly exchange information. Through the simplicity of textbased communication, the Internet has transcended physical distances, enabling a seamless flow of ideas and conversations. This evolution in texting has not only connected people globally but has also catalyzed a dynamic cultural exchange.  \nThe convergence of diverse linguistic expressions in digital conversations highlights the Internet’s role as a universal platform, fostering a global community engaged in continuous dialogue and information sharing.  \nOne striking phenomenon that has gained immense popularity is the practice of transliteration, where users employ the English alphabet to type in their native languages. For example, a Hindi speaker might type ”how are you” as ”ap kaise ho,” using English characters to represent Hindi words. This practice has become ubiquitous, offering a convenient means for multilingual communication. However, this phenomenon poses a unique challenge in the realm of language technology- the n","cbCaiqoyXn2VTQ6q","https://ap.wps.com/l/cbCaiqoyXn2VTQ6q","pdf",464064,"English","# Introduction\n# Related Work\n# Model Architecture\n# Methodology and Dataset\n# Experimental Results\n# Applications and Future Work","[{\"question\":\"Why is language identification difficult for transliterated texting?\",\"answer\":\"Because users write native-language messages using the English alphabet, the surface form no longer directly reflects the original language, making detection challenging for language technology.\"},{\"question\":\"What dataset and languages are used in the study?\",\"answer\":\"The dataset contains phone text messages with transliterated Hindi and Russian written in English characters.\"},{\"question\":\"How does the proposed system identify and convert transliterated text?\",\"answer\":\"It uses BERT to classify the transliterated input into the correct language label, and it employs a Google Translate API workflow to convert transliterated Hindi/Russian back into standard native scripts.\"}]","Language Detection for Transliterated Content - BERT-Based Approach | PDF"]