<p>Handwritten paper charting remains the primary method of collecting anesthesia health data in low-middle income countries that lack automated data capture devices and electronic medical records. Lack of digital data precludes automated evaluation of a patient’s condition in real time intraoperatively and limits healthcare research to improve outcomes. In this paper, we demonstrate the use of computer vision software to digitize handwritten intraoperative data from smartphone photographs of paper anesthesia records from the University Teaching Hospital of Kigali. We implement approaches for removing perspective distortions from photographs, removing shadows and improving image readability though morphological operations, reading handwritten symbols using deep neural networks, and decoding handwritten symbols into meaningful values. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.17mmHg under realistic photography conditions, and digitizes physiological data to within human accuracy when provided legible handwriting and high image quality. Our contributions provides improved access to digital data to healthcare practitioners in low-middle income countries and enables collection of data previously inaccessible to clinicians and researchers. </p>