Faculty Publications

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    Virtual slate: Microsoft kinect based text input tool to improve handwriting of people
    (Asia-Pacific Society for Computers in Education No. 300, Jhongda Road, Jhongli City, Taoyuan County 32001, 2016) Ashwin, T.S.; Sreenivasan, K.; Rameez, M.A.; Varma, A.; Mohandoss, V.; Guddeti, G.
    Text input is a mundane activity that is very closely associated with Human Computer Interaction. In this paper, using the object tracking facility of the Microsoft Kinect sensor and Tesseract for Optical Character Recognition (OCR), we made it possible to write the text by moving our finger in the air as though we were writing on a virtual slate. One of the main purposes of this proposed work is to help the children so that they can improve their handwriting without somebody to check and monitor their writing activity continuously. © 2016 Asia-Pacific Society for Computers in Education. All rights reserved.
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    Handwritten Text Recognition from an Image with Android Application
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mule, H.; Kadam, N.; Naik, D.
    Nowadays, Storing information from handwritten documents for future use is becoming necessary. An easy way to store information is to capture handwritten documents and save them in image format. Recognizing the text or characters present in the image is called Optical Character Recognition. Text extraction from the image in the recent research is challenging due to stroke variation, inconsistent writing style, Cursive handwriting, etc. We have proposed CNN and BiLSTM models for text recognition in this work. This model is evaluated on the IAM dataset and achieved 92% character recognition accuracy. This model is deployed to the Firebase as a custom model to increase usability. We have developed an android application that will allow the user to capture or browse the image and extract the text from the picture by calling the firebase model and saving text in the file. To store the text file user can browse for the appropriate location. The proposed model works on both printed and handwritten text. © 2022 IEEE.