Abstract
The paper proposes a new Optical Character Recognition (OCR) model for medical documents. This model uses advanced machine learning and deep learning to improve the accuracy of extracting text from scanned medical images and various other documents. The goal is to make medical records more accessible and easier to manage electronically. The model can handle both English and multiple other languages, making it suitable for global healthcare use. It also explores adding Text-to-Speech functionality to convert the extracted text to audio, improving accessibility for users with visual impairments. By combining these features with insights from previous OCR and Text-to-Speech research, the model aims to streamline healthcare workflows and improve accessibility for everyone.
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