English handwriting recognition based on the convolutional neural network

Research Article
Open access

English handwriting recognition based on the convolutional neural network

Shengpei Le 1*
  • 1 Binhai International Cooperative School, No.11 Mingyue Road, Binhai New Town, Ningbo, Zhejiang Province, China 315800    
  • *corresponding author 15020440207@xs.hnit.edu.cn
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Handwriting recognition is widely used in the retrieval, recognition, and management of various information to improve efficiency in various industries. Convolutional neural networks help people get rid of the feature of extracting information feature sets manually and significantly improve recognition efficiency. In this paper, a generalization-enhanced network recognition model is proposed using an improved lightweight convolutional neural network model. An improved method is used to adapt word recognition by the idea of pre-recognition segmentation. In addition, the generalization is enhanced by diversifying and pre-processing the dataset so that the algorithm can obtain noise-resistant performance and detail retention and allow the recognition system to recognize various types of scenes. The results show that the model achieves an accuracy of 93% on the test set. Compared with other classical network models, the model has higher recognition accuracy, faster convergence, and better generalization ability. The system elaborated in this paper can be used for devices with weak computer processing power.

Keywords:

manuscript recognition, image processing.

Le,S. (2023). English handwriting recognition based on the convolutional neural network. Applied and Computational Engineering,5,146-151.
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References

[1]. Yu, Q 2019 Semantic segmentation of intracranial hemorrhages in head CT scans [C]. In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) (pp. 112-115). IEEE.

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[4]. Tang W C 2009 Handwritten English character recognition system [D], Doctoral dissertation Shenyang University of Technology.

[5]. Marti, U V et al. 2002 The IAM-database: an English sentence database for offline handwriting recognition [J]. International Journal on Document Analysis and Recognition, 5(1), 39-46.

[6]. T de Campos. 2012. The Chars74K dataset [R]. http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/

[7]. Gregory C. 2017. EMNIST [R]. https://rds.westernsydney.edu.au/Institutes/MARCS/BENS/EMNIST/emnist-gzip.zip

[8]. Andrea G et al. 2006. Wavelet based image segmentation [C] Annual Conference Technical Computing. 2006, (14th)

[9]. Analyticsvidhya. 2021. The architecture of lenet 5 [R], https://www.analyticsvidhya.com/blog/2021/03/the-architecture-of-lenet-5/

[10]. Zhang Z 2018 June Improved adam optimizer for deep neural networks [C], In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (pp. 1-2)

[11]. Kuo and C C J 2016 Understanding convolutional neural networks with a mathematical model [J], Journal of Visual Communication and Image Representation, 41, 406-413.

Cite this article

Le,S. (2023). English handwriting recognition based on the convolutional neural network. Applied and Computational Engineering,5,146-151.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Yu, Q 2019 Semantic segmentation of intracranial hemorrhages in head CT scans [C]. In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) (pp. 112-115). IEEE.

[2]. Machine Learning. 2022. Google Machine Learning Education [R]. https://developers.google.com/machine-learning/practica/imageclassification/convolutional-neural-networks

[3]. Packt Hub. 2022 What is LSTM? [R], packtpub.com.

[4]. Tang W C 2009 Handwritten English character recognition system [D], Doctoral dissertation Shenyang University of Technology.

[5]. Marti, U V et al. 2002 The IAM-database: an English sentence database for offline handwriting recognition [J]. International Journal on Document Analysis and Recognition, 5(1), 39-46.

[6]. T de Campos. 2012. The Chars74K dataset [R]. http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/

[7]. Gregory C. 2017. EMNIST [R]. https://rds.westernsydney.edu.au/Institutes/MARCS/BENS/EMNIST/emnist-gzip.zip

[8]. Andrea G et al. 2006. Wavelet based image segmentation [C] Annual Conference Technical Computing. 2006, (14th)

[9]. Analyticsvidhya. 2021. The architecture of lenet 5 [R], https://www.analyticsvidhya.com/blog/2021/03/the-architecture-of-lenet-5/

[10]. Zhang Z 2018 June Improved adam optimizer for deep neural networks [C], In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (pp. 1-2)

[11]. Kuo and C C J 2016 Understanding convolutional neural networks with a mathematical model [J], Journal of Visual Communication and Image Representation, 41, 406-413.

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