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Using Online Handwritten Character Recognition in Assistive Tool for Students with Hearing and Speech Impairment

Published: 03 May 2020 Publication History

Abstract

Students with hearing and speech impairment are approximately two years behind school due to their disability and the lack of research, few resources and documented practices on the aforementioned conditions that may provide guidance for classroom teachers and Persons with Disabilities (PWDs). Assistive technology helps resolve problems faced by people with physical, mental, intellectual and sensory impairments. It is any device, software or equipment that helps people work around their disability. This paper concentrates on implementing online handwritten character recognition (OHCR) in the web-based learning application as an assistive tool in developing the learning process of deaf and hard-of-hearing (HoH) students. OHCR is the ability of a computer to receive and interpret the handwriting input wherein the movement of the stylus or fingertip can be sensed as it accepts a string of coordinate pairs from the stylus or finger touching a pressure sensitive digital tablet. The OHCR feature in the application will aid students in learning how to complete a given basic Statistics problem and bypass an area of difficulty to achieve the academic outcome intended. Testing shows that the developed system can read 95 characters including numbers, lower and upper case letters, and special characters.

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  • (2021)Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy2021 Innovations in Intelligent Systems and Applications Conference (ASYU)10.1109/ASYU52992.2021.9598983(1-6)Online publication date: 6-Oct-2021

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    IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning
    January 2020
    441 pages
    ISBN:9781450372947
    DOI:10.1145/3377571
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 May 2020

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    Author Tags

    1. Assistive Technology
    2. E-learning
    3. Filipino Sign Language
    4. Online Handwritten Character Recognition

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    • (2021)Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy2021 Innovations in Intelligent Systems and Applications Conference (ASYU)10.1109/ASYU52992.2021.9598983(1-6)Online publication date: 6-Oct-2021

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