Published March 30, 2023 | Version CC BY-NC-ND 4.0
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Digital Text to Users Handwriting (தமிழ்)

  • 1. Department of Computer Science and Engineering, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya Deemed to be University, Kanchipuram, India.

Contributors

Contact person:

  • 1. Department of Computer Scinece and Engineering, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya Deemed to be University, Kanchipuram, India.

Description

Abstract: Converting digital text to handwriting is a simple process because of the abundance of software and websites that do it, like texttohandwriting.com. The Text to Handwriting Converter is a free artificial intelligence-based tool that translates computer text into handwritten text with ease. An individual's handwriting format is saved as an input, converted into text, and then shown as an output. Image processing techniques can be used to process the handwriting. It is possible to use the alphabets of specific languages, such as Tamil (தமிழ்), English, etc. The text of the input is finally displayed in the user's unique handwriting style. It will be useful in numerous ways, including helping the students who have been injured during an accident and it will also reducing the need for paper. Instead of using paper, we can preserve it and refer to it whenever needed. The primary goal of this project is to convert digital text into user handwriting in Tamil (தமிழ்), as it is the oldest language in India and there are currently no websites or apps that accomplish this specifically in Tamil (தமிழ்). There are 247 Tamil (தமிழ்) letters, which are divided into four groups: uyireluttu (உயிரெழுத்து) (12), meyyeluttu (ரெய்ரயழுத்து) (18), uyirmeyyeluttu (உயிெ்ரெய்ரயழுத்து) (216), and finally ayutha eluttu (ஆய்த எழுத்து) (1). A database is created using the handwriting of the person whose handwriting is being converted. These databases consist solely of 247 letters written in that person's handwriting.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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Related works

Is cited by
Journal article: 2231-2307 (ISSN)

References

  • Dasgupta, Poorna Banerjee. "Human Behavioral Analysis Based on Handwriting Recognition and Text Processing." International Journal of Computer Trends and Technology 64 (2018)
  • Yang, Junqing, Peng Ren, and Xiaoxiao Kong. "Handwriting text recognition based on faster R-CNN." 2019 Chinese Automation Congress (CAC). IEEE, 2019
  • Tejasree Ganji et al 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1042 012026, DOI 10.1088/1757-899X/1042/1/012026
  • Khandelwal, Yash. "HANDWRITING RECOGNITION: EXTRACTING TEXT FROM IMAGE AND CONVERTING IT TO DIGITAL FORMAT."
  • Gu, Jiseong, and Geehyuk Lee. "Towards More Direct Text Editing with Handwriting Interfaces." International Journal of Human–Computer Interaction (2022): 1-16.
  • Digitization of Handwritten text using Deep Learning Sumita Gupta; Aditya Gupta; Simran Khanna; Shivam Arora
  • T. Bluche, J. Louradour and R. Messina, "Scan Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention", 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1050-1055, 2017, 2017.

Subjects

ISSN: 2231-2307 (Online)
https://portal.issn.org/resource/ISSN/2231-2307#
Retrieval Number: 100.1/ijsce.A35880313123
https://www.ijsce.org/portfolio-item/A35880313123/
Journal Website: www.ijsce.org
https://www.ijsce.org
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org