TY - GEN
T1 - Doctor's Cursive Handwriting Recognition System Using Deep Learning
AU - Fajardo, Lovely Joy
AU - Sorillo, Nino Joshua
AU - Garlit, Jaycel
AU - Tomines, Cia Dennise
AU - Abisado, Mideth B.
AU - Imperial, Joseph Marvin R.
AU - Rodriguez, Ramon L.
AU - Fabito, Bernie S.
PY - 2019/11/29
Y1 - 2019/11/29
N2 - Handwriting is a skill to express thoughts, ideas, and language. Over the years, medical doctors have been well-known for having illegible cursive handwritings and has been a generally accepted matter. The datasets used in this paper are samples of doctors cursive handwriting collected from several clinics and hospitals of Metro Manila, Quezon City and Taytay, Rizal. In this paper, we present the Handwriting Recognition System using Deep Convolutional Recurrent Neural Network that is developed in order to identify the text in the image of prescriptions written by the doctors and show the readable text conversion of the cursive handwriting. In this study two models were evaluated and based on the experimentation CRNN with model-based normalization scheme than the CRNN alone. This study achieved 76% training accuracy rate and the developed model was found successfully implemented in a mobile application, having achieved a validation accuracy of 72% for the validation set from the remaining 540 images of prescription. The mobile application was validated for the second time using the captured 48 handwriting samples written by the researchers and correctly identified 17 images out of 48 this gives us a 35% validation accuracy.
AB - Handwriting is a skill to express thoughts, ideas, and language. Over the years, medical doctors have been well-known for having illegible cursive handwritings and has been a generally accepted matter. The datasets used in this paper are samples of doctors cursive handwriting collected from several clinics and hospitals of Metro Manila, Quezon City and Taytay, Rizal. In this paper, we present the Handwriting Recognition System using Deep Convolutional Recurrent Neural Network that is developed in order to identify the text in the image of prescriptions written by the doctors and show the readable text conversion of the cursive handwriting. In this study two models were evaluated and based on the experimentation CRNN with model-based normalization scheme than the CRNN alone. This study achieved 76% training accuracy rate and the developed model was found successfully implemented in a mobile application, having achieved a validation accuracy of 72% for the validation set from the remaining 540 images of prescription. The mobile application was validated for the second time using the captured 48 handwriting samples written by the researchers and correctly identified 17 images out of 48 this gives us a 35% validation accuracy.
KW - Deep Convolutional Recurrent Neural Network
KW - Doctors Cursive Handwriting
KW - handwriting recognition
KW - image processing
KW - Optical character Recognition
UR - http://www.scopus.com/inward/record.url?scp=85084752235&partnerID=8YFLogxK
U2 - 10.1109/HNICEM48295.2019.9073521
DO - 10.1109/HNICEM48295.2019.9073521
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85084752235
T3 - 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019
BT - 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019
PB - IEEE
CY - U. S. A.
T2 - 11th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019
Y2 - 29 November 2019 through 1 December 2019
ER -