International Journal for Research in Applied Science and Engineering Technology, Mar 31, 2022
Because of the rising use of digital technology in all businesses and in all day-today activities... more Because of the rising use of digital technology in all businesses and in all day-today activities to store and communicate information, recognition systems in writing have become a prominent study topic and development. Humans still require handwriting copies to be converted into digital copies that can be shared and preserved electronically. Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough. Nowadays, technologies that detect handwriting letters, characters, and figures assist people in doing more sophisticated activities that would otherwise take a long time and be costly. The purpose of this project is to turn handwritten notes into typed documents. We aim to transform handwritten English characters into a computer-readable format using a paragraph as an input, process the paragraph with cursive writing and symbols support, and then train a neural network algorithm to recognize and display the text. CNN is the neural network model that we used. The image can be uploaded by the user. To eliminate background noise, the system pre-processes the input. The machine then looks for text sections in the image. The system then displays the text that is contained in the image to the user. To conduct horizontalvertical segmentation, we used OpenCV.
International Journal for Research in Applied Science and Engineering Technology, Mar 31, 2022
Because of the rising use of digital technology in all businesses and in all day-today activities... more Because of the rising use of digital technology in all businesses and in all day-today activities to store and communicate information, recognition systems in writing have become a prominent study topic and development. Humans still require handwriting copies to be converted into digital copies that can be shared and preserved electronically. Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough. Nowadays, technologies that detect handwriting letters, characters, and figures assist people in doing more sophisticated activities that would otherwise take a long time and be costly. The purpose of this project is to turn handwritten notes into typed documents. We aim to transform handwritten English characters into a computer-readable format using a paragraph as an input, process the paragraph with cursive writing and symbols support, and then train a neural network algorithm to recognize and display the text. CNN is the neural network model that we used. The image can be uploaded by the user. To eliminate background noise, the system pre-processes the input. The machine then looks for text sections in the image. The system then displays the text that is contained in the image to the user. To conduct horizontalvertical segmentation, we used OpenCV.
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Papers by Ketaki Dhotre