01983nas a2200193 4500008004100000245009200041210006900133260001200202300001300214490000700227520137600234100002201610700002101632700001701653700002101670700001701691700002001708856006101728 2023 eng d00aConvolutional Neural Networks for Handwritten Text Recognition of Medical Prescription-0 aConvolutional Neural Networks for Handwritten Text Recognition o c12/2023 a117--1240 v213 a

Converting handwritten prescriptions into electronic format offers several advantages and is crucial for modern healthcare systems. It is essential nowadays because of some factors such as – Legibility and Accuracy: Handwritten prescriptions can be challenging to read and interpret; accessibility and Portability: Electronic prescriptions can be easily stored; Decision Support Systems: By digitising prescriptions, healthcare systems can integrate them with electronic health records (EHRs) and utilise decision support systems. Convolutional neural networks (CNNs) are a class of deep learning algorithms that have proven effective in extracting handwritten text from various documents, including medical prescriptions. By leveraging CNNs for handwritten text extraction, healthcare systems can automate the process of digitising prescriptions, reducing manual effort and potential human errors. This enables seamless integration with electronic systems, facilitating better patient care and overall healthcare management. In this paper, we have trained the CNN model for different parameters and observed the accuracy and loss for various parameters. We got a maximum training accuracy of 89% and a maximum testing accuracy of 70%.

1 aShahade, Makarand1 aKulkarni, Mayuri1 aPawar, Vivek1 aChaudhari, Jatin1 aLakade, Yash1 aKotkar, Darshan uhttps://www.dline.info/fpaper/jdim/v21i4/jdimv21i4_3.pdf02491nas a2200193 4500008004100000245009200041210006900133260001200202300001200214490000700226520189900233100002202132700002102154700001702175700002102192700001702213700002002230856004702250 2023 eng d00aConvolutional Neural Networks for Handwritten Text Recognition of Medical Prescription-0 aConvolutional Neural Networks for Handwritten Text Recognition o c12/2023 a117-1240 v213 a

 Converting handwritten prescriptions into electronic format offers several advantages and is crucial for modern healthcare systems. It is essential nowadays because of some factors such as – Legibility and Accuracy: Handwritten prescriptions can be challenging to read and interpret; accessibility and Portability: Electronic prescriptions can be easily stored; Decision Support Systems: By digitising prescriptions, healthcare systems can integrate them with electronic health records (EHRs) and utilise decision support systems. Convolutional neural networks (CNNs) are a class of deep learning algorithms that have proven effective in extracting handwritten text from various documents, including medical prescriptions. By leveraging CNNs for handwritten text extraction, healthcare systems can automate the process of digitising prescriptions, reducing manual effort and potential human errors. This enables seamless integration with electronic systems, facilitating better patient care and overall healthcare management. In this paper, we have trained the CNN model for different parameters and observed the accuracy and loss for various parameters. We got a maximum training accuracy of 89% and a maximum testing accuracy of 70%.

1 aShahade, Makarand1 aKulkarni, Mayuri1 aPawar, Vivek1 aChaudhari, Jatin1 aLakade, Yash1 aKotkar, Darshan uhttp://www.dline.info/download.php?sn=3899