Application of Fuzzy Matching Algorithms for Doctors Handwriting Recognition

Doctor's handwritten prescriptions are often known to be indecipherable. Uncertainty in medical terms can have dire consequences. A method to effectively recognize medicine names written in doctor's handwriting is proposed in this paper. A corpus of 600 images is compiled with the help of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2022 IEEE Bombay Section Signature Conference (IBSSC) S. 1 - 5
Hauptverfasser: Patil, Riya, Peshave, Prasad, Kamble, Milind
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 08.12.2022
Schlagworte:
CRNN
CTC
Fuzzy matching
Handwriting recognition
Medical services
Predictive models
Recurrent neural networks
Scalability
Text recognition
Uncertainty
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Doctor's handwritten prescriptions are often known to be indecipherable. Uncertainty in medical terms can have dire consequences. A method to effectively recognize medicine names written in doctor's handwriting is proposed in this paper. A corpus of 600 images is compiled with the help of multiple doctors. An exhaustive list of 50 medicines is used for the same. Recognition is performed using the Convolutional Recurrent Neural Network (CRNN) - Connectionist Temporal Classification (CTC) model which results in 93.3 % accuracy. In order to deal with errors produced in the recognized text, edit distance methods are further implemented and analyzed. Damerau-Levenshtein distance method is deemed to be the most suitable, yielding a well-grounded system for medicine name recognition.
DOI:10.1109/IBSSC56953.2022.10037486