| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01022 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001022 | |
| Published online | 06 October 2023 | |
Automated Handwritten Text Recognition
1 Department of CSE (AIML), GRIET, Hyderabad, Telangana State, India
2 Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
* Corresponding author: ramkumar1695@grietcollege.com
A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes the evolution of handwriting more complex. It is difficult to train an Optical Character Recognition (OCR) system using these requirements. In order to convert handwritten material into digital form, this work aims to categorize each unique handwritten word. Because Convolutional Neural Networks (CNNs) are so good at this task, they are the best method for handwriting recognition system. The method will be used to identify writings in various formats.
© The Authors, published by EDP Sciences, 2023
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