International Journal of Scientific Research and Engineering Development

While the translation of languages was conventionally a task of rules-based orstatistical systems, more so recently, neural and transformer-based approaches have managed to sporadically amplify translation accuracy and fluidity across languages. This review paper makes a study in-depth of language translation technologies and incites a mobile-first translator app developed with Flutter as a case study. The app integrates text, speech, and image-to-text translation, underpinned by the OCR (Optical Character Recognition), ASR (Automatic Speech Recognition), and TTS (Text-to-Speech) modules. Backed by state-of-the-art APIs and a modern UI/UX, the system supports over 100 languages, giving enough freedom to the user to detect a language on the fly, copy or share results, and listen to voice with natural-intonation quality. Experimental results exhibited the technology's suitability for handling a high-resource language with enhanced accuracy, whereas drawbacks were established with respect to low-resource languages, unfamiliarity with accent, and the invariable dependence on the internet. The review also shed light on the various persisting flaws of the existing systems, such as the complications of contextual ambiguity, resource constraints, and privacy issues. Finally, some future directions have been enumerated, including offline translation models, multimodal processing, personalization, and augmented reality integration. Hence, the findings convincingly demonstrate that the amalgamation of AI with mobile-first design could greatly improve accessibility, usability, and inclusivity in language translation applications.
Communication beyond language barriers has become an elementary necessity in education, business, medicine, or otherwise casual interaction in an increasingly globalized setup. Limitations on accuracy and fluency features, such models didn't always carry contextual meaning-the traditional translator. It has evolved, with neural networks and, of late, transformer-based architectures, to become sophisticated translation tools capable of operating in multiple languages with a higher degree of accuracy and scalability.