Interpretation of Doctors notes using Deep Learning

The Interpretation of doctor's prescriptions accurately is a critical task in healthcare, yet handwritten prescriptions often pose challenges for automated recognition systems.In this project, we propose a deep learning approach utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) networks to predict the sequence of text written in prescription images.The Bi-LSTM model is trained on a dataset of handwritten prescription images and corresponding text data, employing preprocessing techniques to enhance model performance.Through extensive experimentation and evaluation, our model demonstrates promising results in accurately interpreting prescription text, achieving high accuracy and robustness in recognizing handwritten medical terminology.The project not only addresses the practical need for automated prescription interpretation but also showcases the efficacy of deep learning techniques, particularly Bi-LSTM networks, in handling complex handwritten medical documents.Overall, this research contributes to advancing the capabilities of automated systems in healthcare, with implications for improving patient safety and healthcare efficiency.