Evaluating Methods for Optical Character Recognition on a Mobile Platform : comparing standard computer vision techniques with deep learning in the context of scanning prescription medicine labels

Abstract: Deep learning has become ubiquitous as part of Optical Character Recognition (OCR), but there are few examples of research into whether the two technologies are feasible for deployment on a mobile platform. This study examines which particular method of OCR would be best suited for a mobile platform in the specific context of a prescription medication label scanner. A case study using three different methods of OCR – classic computer vision techniques, standard deep learning and specialised deep learning – tested against 100 prescription medicine label images shows that the method that provides the best combination of accuracy, speed and resource using has proven to be standard seep learning, or Tesseract 4.1.1 in this particular case. Tesseract 4.1.1 tested with 76% accuracy with a further 10% of results being one character away from being accurate. Additionally, 9% of images were processed in less than one second and 41% were processed in less than 10 seconds. Tesseract 4.1.1 also had very reasonable resource costs, comparable to methods that did not utilise deep learning.