Handwriting is the way to convey an idea or information through written means. But over the years, due to fewer doctors per population ratio, doctors have become well-known for their illegible cursive handwriting and have become well accepted. The legibility issue of handwritten medical documents, particularly those created by physicians, has long been a significant problem in healthcare. This study presents Doctor’s Handwriting Recognition, an innovative solution to tackle the problem of illegible doctor’s handwriting in medical records. Our ideation surpasses its function as a recognition system, serving as evidence of technology’s ability to unite tradition and innovation in healthcare documentation. Digitising medical records is essential for improving patient care, optimising operations, and safeguarding data. The recognition system uses a Region-based deep Region-based Convolutional Neural network (R-CRNN) that is enhanced with the Connectionist Temporal Categorical (CTC) loss function. This allows the system to adapt to the unique handwriting of individual doctors. Doctor’s Handwriting Recognition has the potential to revolutionise healthcare professionals’ interactions with handwritten medical information. It offers increased efficiency, enhanced patient safety, and decreased medical errors. Adopting this technological advancement improves healthcare documentation and enhances the accessibility of medical records, ultimately benefiting patient well-being.