Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels

Kseniia Gromova, Vinayak Elangovan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Patient compliance with prescribed medication regimens is critical for maintaining health and managing disease and illness. To encourage patient compliance, multiple aids, like automatic pill dispensers, pill organizers, and various reminder applications, have been developed to help people adhere to their medication regimens. However, when utilizing these aids, the user or patient must manually enter their medication information and schedule. This process is time-consuming and often prone to error. For example, elderly patients may have difficulty reading medication information on the bottle due to decreased eyesight, leading them to enter medication information incorrectly. This study explored methods for extracting pertinent information from cylindrically distorted prescription drug labels using Machine Learning and Computer Vision techniques. This study found that Deep Convolutional Neural Networks (DCNN) performed better than other techniques in identifying label key points under different lighting conditions and various backgrounds. This method achieved a percentage of Correct Key points PCK @ 0.03 of 97%. These key points were then used to correct the cylindrical distortion. Next, the multiple dewarped label images were stitched together and processed by an Optical Character Recognition (OCR) engine. Pertinent information, such as patient name, drug name, drug strength, and directions of use, were extracted from the recognized text using Natural Language Processing (NLP) techniques. The system created in this study can be used to improve patient health and compliance by creating an accurate medication schedule.

Original languageEnglish (US)
Pages (from-to)852-864
Number of pages13
JournalMachine Learning and Knowledge Extraction
Issue number4
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Engineering (miscellaneous)


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