Sign language is the fundamental communication language of deaf people. Efforts to develop sign language generation systems can make the life of these people smooth and effortless. Despite the importance of sign language generation systems, there is a paucity of a systematic literature review. This is the foremost recognizable scholastic literature review of sign language generation systems. It presents a scholastic database of the literature between 1998 and 2020 and suggests classification criteria to systematize research studies. Four hundred fourteen research studies were recognized and reviewed for their direct pertinence to sign language generation systems. One hundred sixty-two research studies were subsequently chosen, examined, and classified. Each of the 162 chosen research papers was categorized based on 30 sign languages and was further comparatively analyzed based on seven comparison parameters (input form, translation technologies, application domain, use of parsers/grammars, manual/non-manual features, accuracy, and output form). It is evident from our research findings that the majority of research on sign language generation was carried out using data-driven approaches in the absence of proper grammar rules and generated only manual signs. This research study may provide researchers a roadmap toward future research directions and facilitate the compilation of information in the field of sign language generation.

Development of Indian Sign Language Dictionary using Synthetic Animations

People do not always use Unicode, rather, they mix multiple languages. The processing of codemixed data becomes challenging due to the linguistic complexities. The noisy text increases the complexities of language identification. The dataset used in this article contains Facebook and Twitter messages collected through Facebook graph API and twitter API. The annotated English Punjabi code mixed dataset has been trained using a pipeline Dictionary Vectorizer, N-gram approach with some features. Furthermore, classifiers used are Logistic Regression, Decision Tree Classifier and Gaussian Naïve Bayes are used to perform language identification at word level. The results show that Logistic Regression performs best with an accuracy of 86.63 with an F-1 measure of 0.88. The success of machine learning approaches depends on the quality of labeled corpora.

Machine Translation problem is an open area of research. It is a challenge to build a domain independent, effective and efficient MT System. The major approaches to machine translation encountered in literature are Word-for-Word Translation, Syntactic Transfer, Semantic Transfer, Interlingua Approach, Example Based Translation, and Statistical Approach to Translation. The existing Hindi to Punjabi MT systems have been developed using rule bases approach and word-for-word translation approach. The present paper is an attempt to study the Architecture of Hindi to Punjabi MT System based on Statistical Approach.

Machine Translation (MT) is the process to translate the text from one natural language (source) to another natural language (target), with the help of machine. Machine translation is related to the fields of artificial intelligence, linguistics, statistics and mathematics. It is also related to natural language processing (NLP). In the process of machine translation various approaches are to be used. Each and every approach has its own benefits and drawbacks.