Deep Neural Network Based Cross Lingual Recognition and Translation for Kannada and English

Abstract

The rapid growth of multilingual digital communication has increased the need for intelligent systems capable of recognizing and translating text written in multiple languages. In India Kannada and English are widely used in educational, administrative and social environments. Automatic recognition of handwritten and printed text in these languages remains a challenging task due to script diversity structural complexity and writing style variations. Conventional Optical Character Recognition systems are generally limited to single-language processing and often fail to provide accurate recognition for multilingual, cross language and handwritten documents. This paper proposes a deep learning-based framework for cross lingual recognition and translation of Kannada and English text. The proposed system integrates Convolutional Neural Networks for visual feature extraction Bidirectional Long Short Term Memory networks for contextual sequence modelling and Connectionist Temporal Classification for segmentation-free text decoding. A transformer-based translation module is further incorporated to translate recognized text into target languages. The framework is designed to support both handwritten and printed text recognition with multilingual and cross lingual translation capability. The proposed approach is expected to improve recognition accuracy, translation quality and multilingual document processing efficiency. The system can be effectively applied in education, administration, digital archiving and assistive technologies.