Hindi, Hinglish: Head to Head

In the framework of the Indian Constitution, the coordination of Hindi and other Indian languages is being reinforced by strong technological foundations. Institutions such as E-Governance, Digitalization, Unicode, and C-DAC are providing multilingual software, thereby promoting linguistic integration. Today, apart from Hindi, other Indian languages like Bengali, Tamil, Marathi, Kannada, and Telugu are witnessing vibrant publication of journals and magazines. Alongside the Hindi and Urdu Academies, organizations such as the Tamil Sangam, Kannada Sahitya Parishad, and Marathi Sahitya Parishad represent encouraging signs for the bright future of Hindi and other Indian languages. Many such positive developments are emerging in this direction. Institutions like the Central Hindi Directorate and the National Translation Mission are working to strengthen the bond between Hindi and other Indian languages. Indeed, all Indian languages together seem to fortify the unity of India’s multilingual cultural identity.

Many different languages are spoken in India, each language being the mother tongue of tens of millions of people. While the languages and scripts are distinct from each other, the grammar and the alphabet are similar to a large extent. One common feature is that all the Indian languages are phonetic in nature. In this paper we describe the development of a transliteration scheme Om which exploits this phonetic nature of the alphabet. Om uses ASCII characters to represent Indian language alphabets, and thus can be read directly in English, by a large number of users who cannot read script in other Indian languages than their mother tongue. It is also useful in computer applications where local language tools such as email and chat are not yet available. Another significant contribution presented in this paper is the development of a text editor for Indian languages that integrates the Om input for many Indian languages into a word processor such as Microsoft Win Word®. The text editor is also developed on Java® platform that can run on Unix machines as well. We propose this transliteration scheme as a possible standard for Indian language transliteration and keyboard entry.

Named Entity Recognition (NER) is a process of identification and classification of names into pre-defined categories- person, location, organization, date, time and others. NER serves as one of the most valuable application tools for varying languages and domains. Despite its popularity and successful deployment in English, this area is still exploratory for the Hindi language. NER in Hindi is a challenging task due to the scarceness of language resources and complex morphological structure. An initial impetus to write this chapter constitutes concise research on prevailing NER systems in the Hindi language. To achieve this objective, all-inclusive review and analysis of the research trends for NER in Hindi starting from the year 1999 to till date is conducted from varied articles. These articles include full-length articles, survey papers, dissertations, and guidelines which exploit decision analysis of the NER with respect to six vital aspects. All these aspects are collaborated and visualized in details within the chapter. The future research directions, challenges, and open issues in the field of NER are also presented for keen researchers. One can then design a unified, coherent NER system which can be applied to vivid language processing tasks for the Hindi and other Indian languages.

Hindi is the national language of India. However, most of the Government records, resolutions, news, etc. are documented in English which remote villagers may not understand. This fact motivates to develop an automatic language translation system from English to Hindi. Machine translation is the process of translating a text in one natural language into another natural language using computer system. Grammatical structure of Hindi language is very much complex than English language. The structural difference between English and Hindi language makes it difficult to achieve good quality translation results. In this paper, phrase-based statistical machine translation approach (PBSMT) is used for translation. Translation, reordering and language model are main working components of a PBSMT system. This paper evaluates the impact of various combinations of these PBSMT system parameters on automated English to Hindi language translation quality. Freely available n-gram-based BLEU metric and TER metric are used for evaluating the results.

Renowned Urdu satirist, Mujtaba Hussain, reads selections from his extensive catalog of Urdu journalism and humor to an audience composed of Hindi Urdu Flagship students and the wider South Asian studies community at UT Austin. The recording should be wonderful listening comprehension practice for intermediate to advanced students of Urdu and/or Hindi. Mujtaba Hussain is a prolific and critically acclaimed Urdu journalist and creative writer. Over the past forty years he has produced fifteen volumes of incisive, humorous journalism and creative writing. His work has been widely translated into English, Hindi and various other regional languages of India. In 2007, he received a Padma Shree, India’s prestigious presidential award, for his contributions to Urdu literature. Kushwant Singh describes Hussain as “rare among Indian writers of humour. While he is unable to say anything unkind about others, he is equally unable to say anything in his own praise. His piece Apnee Yaad Mein is a sort of self written in memoriam. He recalls his younger days in Hyderabad, his love of wandering about on full moonlit nights, playing kabaddi on the first night of his marriage when his friends forced him to go home to his bride. He was commissioned to write light pieces for Siasat on the sudden death of the man who wrote the column. He never looked back. He has reason to rest on his laurels: whenever the subject of humour in Urdu writing comes up, the first name that is mentioned is of Mujtaba Hussain of Hyderabad.”

In this paper we propose support vector machines (SVM) for predicting the durations of the syllables in Indian languages. In this work SVM regression models are used for modeling the durations of the syllables and SVM classification models are used for categorizing the syllables based on duration. Analysis is performed on broadcast news data in the languages Hindi, Telugu and Tamil, in order to predict the duration of syllables in these languages using SVM regression model. The input to the SVM consists of a set of phonological, positional and contextual features extracted from the text. We also propose two-stage duration models for improving the prediction accuracy. From the studies it was found that about 86% of the syllable durations are predicted within 25% of the actual duration. The performance of the duration models are evaluated using objective measures such as mean absolute error (/spl mu/), standard deviation (/spl sigma/) and correlation coefficient (/spl gamma/).

CAPTCHA is an acronym of Completely Automated Turing Test to Tell Computers and Humans Apart which is also referred to as HIP (Human Interaction Proof). It is a computer program that generates challenges or tests that only humans can solve and the machines or automated software cannot solve. CAPTCHAs are used in various Web Applications to restrict bots from doing unwanted activities in areas of protected content and unwanted submissions of web forms. In this paper a Model for Multilingual CAPTCHA challenges which can be more accessible and easy to use for non-English users is proposed. Rendering CAPTCHA tests in regional languages can improve usability and accessibility for non-native speakers of English language. The suggested model can be implemented in all major web programming languages such as PHP, .NET, J2EE, Perl, Ruby etc. I. INTRODUCTION The websites and web applications using CAPTCHA may be usually targeted for a large audience nationally or globally and not limited to a particular location or city. The websites and web applications are therefore used by people with knowledge of different languages and scripts including but not limited to English. Modern websites and web applications and designed in a way that the users can use them in their own languages. In India, there are 22 officially recognized languages and 12 different scripts. Majority of people in India have knowledge of and use their regional languages are not well versed in English. More and more websites and web applications for India are providing content and user interface in the regional languages of India. Many of these websites and web applications contain resources and areas protected by CAPTCHAs. The Government of India has also issued several guidelines for using Hindi and other regional languages in the websites of Government Departments. More and more Govt. Departmental websites are providing content in regional languages. The CAPTCHAs used in these Websites and web applications are English only even if the users choose to use them in a non-English regional language. This leads to very poor accessibility and usability of these websites.

In October 2022, the launch of first year MBBS books in Hindi language in the state of Madhya Pradesh in India kick started the debate on the introduction of Hindi/local language in medical education, which is currently being imparted in English. Opinions have been divided among the supporters of Hindi and local languages as well as opponents of this move. However, several issues need to be addressed before wider implementation of the policy of imparting medical education in Hindi/local languages. Right from infrastructure, human resource, economic impact, academic impact, international effects to legal issues have to be kept in mind and stakeholders must discuss these before implementation. A subsequent reversal of this policy, either through executive orders or judicial intervention, could lead to irreparable loss to students enrolled and trained in Hindi/local language.

Translation and validation of the revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) for evaluating the effectiveness of acute pain services in the Indian population.

Optical Character recognition is superfluity of the various applications of envision of human's life. In present life OCR has been successfully using in finance, legal, banking, health care and home need appliances. OCR is the method of digitalization of hand and type written or printed text into machine-encoded form. India is a multi cultural, literature and traditional scripted country. 18 official scripted languages are formed and have many local regional languages in India. Telugu is official language of the southern Indian states of Telangana and Andhra Pradesh and many people spoken in all over in Malaysia, Bahrain, Oman, Singapore, Fiji, UAE and Mauritius. Telugu is a syllabic language, symbol script represents a complete syllable and formed with the conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and post level of consonants. This paper proposes the layered approach methodology to recognize the characters, conjunct consonants, mixed-conjunct consonants and expressed the efficient classification of the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system controller to take the text in the form of written or printed, collected the text images from the scanned file, digital camera, Processing the Image with Examine the high intensity of images based on the quality ration, Extract the image characters depends on the quality then check the character orientation and alignment then to check the character thickness, base and print ration. The input image characters can classify into the two ways, first way represents the normal consonants and the second way represents conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process starts from middle layer, and then it continues to check the top and bottom layers. The recognition process treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of present base character otherwise treats as normal consonants. The post processing technique applied to all three layered characters. Post processing of the image: concentrated on the image text readability and compatibility, if the readability is not process then repeat the process again. In this recognition process includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In the process of development of the algorithm the pre-processing, segmentation, character recognition and post-processing modules were discussed. The main objectives to the development of this paper are: To develop the classification, identification of deference prototyping for written and printed consonants, conjunct consonants and symbols based on 3 layered approaches with different measurable area by using fuzzy logic and to determine suitable features for handwritten character recognition.

Aim:The aim is to translate and validate the European Organization for Research and Treatment for Cancer (EORTC) ovarian cancer (OC) module (OV-28) into Hindi and Marathi to use for patients and scientific community.Methods and Results:The EORTC OV-28 was translated into Hindi and Marathi languages using prescribed guidelines by the EORTC. The process included forward translation by four translators (2 each for Hindi and Marathi). The questionnaires obtained were then given to independent backward-translators who then translated them back into English. These 2 questionnaires were then compared with the original EORTC questionnaire and the second intermediate questionnaires were formed. The second intermediate questionnaire was subsequently administered in twenty patients (10 each for Hindi and Marathi) diagnosed with OC who had never seen the questionnaire before, for pilot testing. Each of these ten patients after filling up the questionnaire themselves was then interviewed for any difficulty encountered during the filling up of the questionnaires. These were in the form of specific modules including difficulty in answering, confusion while answering, and difficulty to understand, whether the questions were upsetting and if patients would have asked the question in any different way. The suggestions were incorporated into the second intermediate questionnaires to form the final Hindi and Marathi ON-28 questionnaires. These questionnaires were then sent to the EORTC for the final approval to be used in clinical studies.Conclusion:We have successfully translated EORTC OV-28 module into Hindi and Marathi languages, and EORTC approved them to be used in clinical practice and studies for OC patients.

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

As someone who entered the United States as a foreign student in the late 1980s, my academic career was intensely shaped by the then burgeoning discourses on colonialism and postcolonialism.1 The triumvirate of Said, Spivak, and Bhabha functioned like a citation machine; and despite Said’s Orientalism, South Asia, especially India, dominated as the colonial/ postcolonial site par excellence. Much of this had to do not just with the presence of many a South Asian scholar studying, writing, and teaching in U.S. universities but also with the fact that some of the most brilliant work was being undertaken on India: Lata Mani’s work on the discourses on sati, the work of the Subaltern Studies historians, Spivak’s “Can the Subaltern Speak?” and her famous essays on the Rani of Sirmur, and of course Bhabha’s legendary essays “Sly Civility” and “Signs Taken for Wonders.” The outpouring of anglophone fiction from South Asia, topped off by the publication of Rushdie’s Midnight’s Children, made certain that there would be no dearth of work on South Asian (read Indian) postcolonial fiction, and even today, despite a fairer distribution of attention to other parts of the ex-colonial worlds, works on South Asia continue to dominate the scene. Much has been written about this privileging of South Asia (again, read India) and its incarnation as the quintessential postcolonial site, and I am not going to rehearse the debates here. But suffice it to say that while my teaching spans continents, my first book was part and parcel of the work on the discourse of gender and nationalism in colonial and postcolonial literary representations by British, Bengali, and anglophone South Asian writers. But, in another sense the book was comparative because it brought into play writers working in different languages, and I remember getting a report from Cambridge University Press asking me why there was no chapter on Rushdie.

Urdu script is derived from the Arabic script but Urdu differs from Arabic in its nature and unlike Arabic some sounds are pronounced like each other. Due to that some experts object to Urdu script and suggest the removal of letters with similar sounds. But the removal of these letters can create many complexities and difficulties which cannot be resolved. These eight specific sounds of Urdu are common to Pakistani languages ​​such as Punjabi, Sindhi, Pashto, Balochi, Kashmiri, and other local languages. How is it possible to exclude these words from all these languages? These sounds are the valuable capital of world languages ​​like Persian and Arabic. With this collaboration, Urdu emerges as a great global and scientific language. By removing these letters, Urdu will suffer a great loss in terms of knowledge. There will be an encounter, which cannot be compensated. In short, this change is not feasible in context with Urdu historical background and present linguistic geographical condition. The Urdu script is non- diacritical in its nature and diacritics in Urdu are not arranged in the same way as in Arabic. However, it is necessary to organize the essential diacritics in the basic textbooks. This article argues and analyses the justification of Urdu letters with similar sounds and diacritics in detail.

India is a linguistically rich country having eighteen constitutional languages, which are written in ten different scripts. Indian languages are highly inflectional with a rich morphology, relatively free word order, and default sentence structure as subject object verb. Many of them are structurally similar called sibling languages. Hindi and Gujarati languages are such siblings. The paper briefly describes GH-MAP; a rule based token mapping system developed by us for translation between sibling language pair Hindi and Gujarati. GH-MAP system performs effective word-for-word translation using simple and computationally inexpensive methods and minimal lexical resources. Issues of syntactic, semantic and structural divergence in translation using GH-MAP are resolved with the help of special empirical rules. The aim of GH-MAP is not to produce high quality translation in the sense of linguistics; rather it has been developed to produce correct working translation sufficient to cross the language barrier. The system was evaluated on the test bed obtained from FIRE 2010, literature on Gandhiji and ELRA-W0037. For establishing relevance of the model, ‘into-Gujarati’ BLEU, PER and METEOR score have been calculated.