OCR-based Mobile Medication Prescription Bag Reader

• Main Authors: Yen-Yu Lin, 林彥佑
• Other Authors: Sheng-Luen Chung
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/38618556574725146629

碩士 === 國立臺灣科技大學 === 電機工程系 === 103 === Critical to the effectiveness of medical treatment and allocation of medical resource, medication adherence is one of paramount importance for elder people, due to their tendency of suffering multiple chronicle illness and inevitable cognition impairment. This study proposes an OCR-based Mobile Srescription Bag Reader for Elders to enhance medication adherence. In contrast to conventional medication reminder designs which rely keypad or handwriting for the input interface, the proposed solution allows the elder to take pictures of prescriptions as most natural input. The picture that contains medication details is then processed by Optical Characteristics Recognition (OCR) to extract medication information for later automatic reminding notification. To this aim, several key techniques are utilized and adapted: image processing, OCR, context extraction, and mobile programming in tackling the following issues: (1) Preprocess of the prescription pictures, taken in general angles and lightening conditions, to facilitate subsequent OCR; (2) Extraction and decipherment of prescriptions from difference hospitals and clinics for information relating to names of medicine and regiment instruction from the prescription image; (3) Enhancement of OCR performance by context correction method that fits recognition results into correct vocabulary and contexts of medical prescriptions; (4) Design and implementation of an elder friendly Android APP that relies on picture taking for medical prescription recognition without posing too much constraint. In general, the aforementioned techniques of integrating OCR and context extraction technique developed can also be applied to more general context-oriented image applications. To demonstrate the validity of the proposed solution, medical prescriptions from eight hospitals are tested by our App. On top of that, further functions can be achieved, like prompting medication in-taking reminding and recording medication intaking history, which later can be used for subsequent prescriptions in achieving individual care and shared decision-making medications.