HiEn: Speak the Switch

Abstract

Linguistic barriers remain a major challenge in communication, education, and cultural preservation, particularly for ancient and low-resource languages. While modern neural machine translation systems perform efficiently for widely used languages, they lack sufficient support for historical scripts such as Brahmi and classical languages due to limited datasets. This paper presents HiEn: Speak the Switch, a multimodal multilingual conversion system that integrates text, image, and voice-based translation within a unified platform. The system combines Optical Character Recognition, speech recognition, text-to-speech synthesis, and machine translation APIs within a Python-based graphical interface. Additionally, a custom character-mapping module enables deterministic conversion of the Brahmi script into modern equivalents. The system demonstrates reliable multilingual translation and satisfactory OCR performance. It enhances accessibility, simplifies translation workflows, and contributes to the digital preservation of ancient scripts. The architecture is scalable and can be extended using advanced transformer-based models in future implementations.