Transcribing and Translating Bilingual Text using OCR Tesseract and Deep Learning

This study investigates the efficacy of integrating OCR Tesseract and deep learning methodologies for the transcription and translation of bilingual text. Leveraging OCR Tesseract as the foundation, we employ deep learning models to enhance the accuracy and reliability of bilingual text processing. The textual and image data for the OCR model are collected from diverse sources, including bilingual-language documents and images. The machine learning techniques are trained on Tesseract OCR, an open-source OCR engine, to enhance recognition accuracy for bilingual text. Deepl, a neural machine translation tool, is used for machine translation, with customization tailored to diverse contextual requirements. The process involves collecting Bilingual text and image data, organizing them into training and validation sets for OCR and machine translation models. The data is then cleaned, normalized, and feature engineered to ensure consistency across the dataset. This process includes removing irrelevant characters, punctuation, formatting inconsistencies, and extracting relevant features like document length and language complexity. Our findings underscore the potential of deep learning driven approaches in bridging the gap between source and target languages effectively, facilitating better preservation of contextual meaning and semantic coherence. Despite certain challenges and limitations, our research lays a solid foundation for further advancements in bilingual text processing, with implications for document digitization, language localization, and cross-cultural communication.