Keyword spotting in doctor's handwriting on medical prescriptions. (15th June 2017)

• Record Type: Journal Article
• Authors: Roy, Partha Pratim; Bhunia, Ayan Kumar; Das, Ayan; Dhar, Prithviraj; Pal, Umapada
• Is Part Of: Expert systems with applications. Volume 76(2017)
• Page Start: 113
• Page End: 128
• Publication Date: 2017-06-15
• Subjects: Word spotting -- Printed/handwritten text separation -- Knowledge extraction -- Hidden Markov model -- Medical prescriptions; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.01.027
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Tandem model is proposed to improve traditional HMM-based word spotting schemes. A novel scheme towards automatic transcription of handwritten medical prescription. Printed text information from prescription is used to improve the performance. Robustness of Tandem feature is shown in multiple scripts. Abstract: In this paper, we propose a word spotting based information retrieval approach for medical prescriptions/reports written by doctors. Sometimes due to almost illegible handwriting, it is difficult to understand the medication reports of doctors. This often confuses the patients about the actual medicine/disease names written by doctors and as a consequence they suffer. A medical prescription is generally partitioned into two parts, a printed letterhead part containing the doctor's name, designation, organization name, etc. and a handwritten part where the doctor writes patient's name and report his/her findings and suggests medicine names. There are many significance impacts of the proposed work. For example, such work can be used (i) to develop expert diagnostic systems (ii) to extract information from patient history that can be obtained by this proposed method (iii) to detect wrong medication (iv) to make different statistical analysis of the medicines prescribed by the doctors etc. To extract the information from such document images, first we extract the domain specific knowledge of doctors by identifying department names from the printed text that appears in letterhead part. From the letterhead text, the specialty/expertise of doctors is understood and this helps us to search only relevant prescription documents for word spotting in handwritten portion. Word spotting in letterhead part as well as in handwritten part has been performed using Hidden Markov Model. An efficient MLP (Multilayer Perceptron) based Tandem feature is proposed to improve the performance. From the experiment with 500 prescriptions, we have obtained encouraging results. Information from printed letterhead part improved the word spotting performance in handwritten part, significantly.