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An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Md. Abdur Rahim1, Fahmid Al Farid2, Abu Saleh Musa Miah3, Arpa Kar Puza1, Md. Nur Alam4, Md. Najmul Hossain5, Sarina Mansor2, Hezerul Abdul Karim2,6,*

1 Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
2 Faculty of Engineering, Multimedia University, Cyberjaya, 63100, Malaysia
3 Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur, 5311, Bangladesh
4 Department of Mathematics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
5 Department of Electrical, Electronic and Communication Engineering, Pabna, University of Science and Technology, Pabna, 6600, Bangladesh
6 Department of Electrical and Communication Engineering, Pabna University of Science and Technology, Pabna, 6600, Bangladesh

* Corresponding Author: Hezerul Abdul Karim. Email: email

(This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)

Computer Modeling in Engineering & Sciences 2024, 140(2), 1689-1710. https://doi.org/10.32604/cmes.2024.048714

Abstract

Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. Our innovative method is intricately designed, encompassing five distinct phases: data collection, preprocessing, feature extraction, significant feature selection, and classification. One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting (BHW), setting the foundation for our comprehensive approach. Post-preprocessing, we embarked on an exhaustive feature extraction process, encompassing integration with kinematic, statistical, spatial, and composite features. This meticulous amalgamation resulted in a robust set of 91 features. To enhance the efficiency of our system, we employed an analysis of variance (ANOVA) F test and mutual information scores approach, meticulously selecting the most pertinent features. In the identification phase, we harnessed the power of cutting-edge deep learning models, notably the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics. Moreover, our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM, capitalizing on fine motor features for enhanced individual classifications. Crucially, our experimental results underscore the superiority of our approach. The CNN, BiLSTM, and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques. This validates our method’s efficacy and underscores its potential to outperform existing technologies, marking a significant stride forward in the realm of individual identification through handwriting analysis.

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Cite This Article

APA Style
Rahim, M.A., Farid, F.A., Miah, A.S.M., Puza, A.K., Alam, M.N. et al. (2024). An enhanced hybrid model based on CNN and bilstm for identifying individuals via handwriting analysis. Computer Modeling in Engineering & Sciences, 140(2), 1689-1710. https://doi.org/10.32604/cmes.2024.048714
Vancouver Style
Rahim MA, Farid FA, Miah ASM, Puza AK, Alam MN, Hossain MN, et al. An enhanced hybrid model based on CNN and bilstm for identifying individuals via handwriting analysis. Comput Model Eng Sci. 2024;140(2):1689-1710 https://doi.org/10.32604/cmes.2024.048714
IEEE Style
M.A. Rahim et al., "An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis," Comput. Model. Eng. Sci., vol. 140, no. 2, pp. 1689-1710. 2024. https://doi.org/10.32604/cmes.2024.048714



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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