Developing a Benchmark Data Set for Optical Character Recognition of Drug Names in Medical Prescriptions - Aldaeb, Maryam; Fadeel, Mohamed (Sebha University Conference Proceedings, 2025)
Interpreting handwritten medication names remains a global challenge that often causes medical errors and delays in drug dispensing. This issue threatens patient safety and demands effective solutions. To address it, a data set was developed of handwritten drug names to support the training of optical character recognition (OCR) systems. The goal is to improve prescription readability, enable digital archiving, and support electronic health records. The data set can also be used in smart health applications to help patients manage their medications. The data set was built using contributions from 250 physicians and medical students in southern region of Libya. Each participant wrote 20 predefined drug names in standardized forms, producing 9,225 handwriting samples. Image processing techniques were used, such as converting color images to grayscale, to reduce memory requirements by reducing the size to one-third of the original size and increasing processing speed by 66%. In addition, input normalization was applied by converting pixel. These techniques accelerate model training, enhance accuracy, and address artifacts caused by different handwriting sizes. This resource aims to reduce prescription-related errors, enhance data accessibility through searchable archives, and support faster and safer medication processing. The paper also proposes a practical approach to integrating intelligent systems in healthcare and highlights the importance of collaboration between clinicians and researchers to improve patient safety and care quality.