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

作　　者：Md.Abdur Rahim Fahmid Al Farid Abu Saleh Musa Miah Arpa Kar Puza Md.Nur Alam Md.Najmul Hossain Sarina Mansor Hezerul Abdul Karim 

机构地区：[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

出　　处：《Computer Modeling in Engineering & Sciences》2024年第8期1689-1710,共22页工程与科学中的计算机建模（英文）

基　　金：MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).

摘　　要：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,B

关 键 词：Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]