Multi-Stroke Handwriting Character Recognition and Enhancing Proficiency with CNN: A Touch-Based Writing Approach-Reference-Cited by-同舟云学术

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Multi-Stroke Handwriting Character Recognition and Enhancing Proficiency with CNN: A Touch-Based Writing Approach

Published:2023-11-01 Issue: Volume:12 Page:1-5
ISSN:
Container-title:2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
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Author:

K Vivekrabinson¹,J Bharath Singh¹,K Ragavan²,S Joshua Kumaresan³,Veerappa Dinesh S Erana⁴,G Mareeswari⁵

Affiliation:

1. Kalasalingam Academy of Research and Education,Department of CSE,Krishnankoil,Tamilnadu,India

2. Vellore Institute of Technology,Department of IoT,Vellore,Tamilnadu,India

3. Saveetha Institute of Medical and Technical Sciences,Department of ECE,Chennai,Tamilnadu,India

4. Ramco Institute of Technology,Department of CSBS,Rajapalayam,Tamilnadu,India

5. Ramco Institute of Technology,Department of IT,Rajapalayam,Tamilnadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10368386/10368609/10369098.pdf?arnumber=10369098

Reference18 articles.

1. Learning a Deep Convolutional Network for Handwriting Recognition;LeCun

2. Handwriting Recognition of Historical Documents with Few Labeled Data

3. Handwritten character recognition using convolutional neural network

4. Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach

5. Segmentation-free writer identification based on convolutional neural network

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