Handwritten English Alphabet Recognition Using Convolutional Neural Network

Authors

  • Dhyanik Pujara Student, Department of Electronics and Communication Engineering, Nirma University, Ahmedabad, Gujarat, India
  • Riya Gautam Student, Department of Electronics and Communication Engineering, Nirma University, Ahmedabad, Gujarat, India

Keywords:

Convolutional neural networks, artificial intelligence, alphabet recognition, machine learning

Abstract

This research paper presents an approach for English alphabet recognition using machine learning. The proposed system utilizes a convolutional neural network (CNN) to identify individual characters within an input image. The dataset used in this research consists of a large collection of handwritten alphabet images, sourced from Kaggle's A-Z Handwritten Alphabets dataset in CSV (comma-separated values) format, which were preprocessed and augmented to improve the model's accuracy. We trained and tested our model using this dataset and achieved a validation accuracy of 97.9% and a training accuracy of 95.8%. The validation loss was 0.0745, and the training loss was 0.1517. Our system outperformed previously published methods for English alphabet recognition. We also tested our model on a real-world data set containing handwritten letters, and the results were promising, with an accuracy of 90%. The proposed system has various applications, including optical character recognition, document digitization, and automated handwriting analysis.

References

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998; 86 (11): 2278–2324.

Roy PP, Banerjee S. Handwritten English character recognition using convolutional neural network. In: 2018 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT), 2018. pp. 1–5.

Zhang L, Hu W, Xiao X, Zhang X. Online handwritten Chinese character recognition using deep learning method. Neurocomputing. 2016; 185: 94–103.

Muhammad K, Rehman A, Javed F. Handwritten Urdu character recognition using deep convolutional neural network. Multimedia Tools Appl. 2019; 78 (7): 8417–8436.

Phung VH, Rhee EJ. A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl Sci. 2019; 9 (21): 4500.

Pranathi K, Jagini NP, Ramaraj SK, Jeyaraman D. Video-based emotion sensing and recognition using convolutional neural network based kinetic gas molecule optimization. Acta IMEKO. 2022; 11 (2): 1–7.

Pal A, Singh D. Handwritten English character recognition using neural network. Int J Computer Sci Commun. 2010; 1 (2): 141–144.

Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Basu DK. Handwritten ‘Bangla’ alphabet recognition using an MLP based classifier. In: Proceedings of 2nd National Conference on Computer Processing of Bangla, Dhaka, Bangladesh, February 2005. pp. 285–291.

Acharya UD, Reddy NVS, Krishnamurthy. Isolated handwritten Kannada numeral recognition using structural feature and K-means cluster, In: IISN, 2007 . pp. 125–129.

Perwej Y, Chaturvedi A. Neural networks for handwritten English alphabet recognition. Int J Computer Appl. 2011; 20 (7): 1–5.

Downloads

Published

2023-10-28

Issue

Vol. 14 No. 3 (2023)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.