Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture

Fatimatelbatoul M. Husari; Mohammad A. Assaad

doi:10.24086/cuesj.v8n2y2024.pp99-103

Fatimatelbatoul M. Husari Department of Informatics and Software Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq https://orcid.org/0000-0001-6553-8440
Mohammad A. Assaad Department of Informatics and Software Engineering, College of Engineering, Cihan University,-Erbil, Kurdistan Region, Iraq https://orcid.org/0000-0002-6697-0275

DOI: https://doi.org/10.24086/cuesj.v8n2y2024.pp99-103

Keywords: Deep learning, feature extraction, handwriting recognition, convolutional neural network, long-short term memory

Abstract

Handwritten character identification finds broad applications in document analysis, digital forensics, and human-computer interaction. Conventional methods encounter challenges in accurately deciphering a range of handwriting styles and variations. Consequently, investigating intelligent system for handwriting identification becomes crucial to enhance accuracy and efficiency. This paper introduces an innovative hybrid deep learning architecture, seamlessly integrating the strengths of convolutional neural networks (CNN) and long-short term memory (LSTM) within a one single framework. The combination of these structures enables the model to effectively capture both spatial features and temporal dependencies inherent in handwritten strokes, resulting in improved recognition performance. The proposed 2DCNN-LSTM algorithm has been tested on MNIST dataset. The proposed hybrid CNN-LSTM structure has been compared to conventional intelligent machine learning methods, and the results demonstrate the superior performance of the hybrid CNN-LSTM, showcasing heightened accuracy, sensitivity, specificity, and other evaluation metrics.

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Author Biographies

Fatimatelbatoul M. Husari, Department of Informatics and Software Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq

Fatimatelbatoul Husari is a Lecturer at the Department of Informatics and Software Engineering, Faculty/College of Engineering, Cihan University-Erbil. Her research interests are in artificial intelligence, deep learning, intelligent systems, intelligent monitoring and diagnosis and data analysis.

Mohammad A. Assaad, Department of Informatics and Software Engineering, College of Engineering, Cihan University,-Erbil, Kurdistan Region, Iraq

Mohammad Anwar Assaad is an Assistant Prof. at the Department of Informatics and Software Engineering, College of Engineering, Cihan University. He got the B.Sc. degree in Syria, the M.Sc. degree in France, and the Ph.D. degree in France. His research interests are in Artificial Intelligence, Machine Learning, Expert Systems, Web Programming and Software Engineering.

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