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- W4383503016 abstract "Handwritten recognition is becoming one of the most researched areas in the field of computer science. As the technologies are growing, everyone wants advanced life, which makes life easier. Even in the recognition of handwriting, mainly doctors notes, they are very difficult for everyone to understand and it takes time for a person to analyse it. So, this paper mainly focused on interpreting doctor’s notes using handwritten recognition and deep learning techniques. The handwritten or printed document pictures are transformed into their electronic counterparts using an optical character recognition (OCR) system. Due to individuals' inconsistent writing styles, dealing with handwritten texts is significantly more difficult than dealing with printed ones. Handwritten text recognition could be done by Image processing, Machine Learning or Deep Learning Techniques. Out of these Deep Learning remains to be the most popular and prominent. Some of the Deep Learning techniques includes Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This paper gives a review of the various handwritten recognition methodologies used for interpreting handwritten texts. This paper includes the most important algorithms that could be used for detecting the handwritten word/text/character by using various approaches for the recognition process. In the end we are thus comparing the accuracies provided by these systems." @default.
- W4383503016 created "2023-07-08" @default.
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- W4383503016 date "2023-04-19" @default.
- W4383503016 modified "2023-09-24" @default.
- W4383503016 title "Interpreting Doctor notes using Handwriting Recognition and Deep Learning Techniques: A Survey" @default.
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- W4383503016 doi "https://doi.org/10.1109/icaecis58353.2023.10170398" @default.
- W4383503016 hasPublicationYear "2023" @default.
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