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

Husari, Fatimatelbatoul M. and Assaad, Mohammad A. (2024) Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture. Cihan University-Erbil Scientific Journal, 8 (2). pp. 99-103. ISSN 2519-6979

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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.

Item Type: Article
Uncontrolled Keywords: Deep Learning, Feature Extraction, Handwriting Recognition, Convolutional Neural Network, Long-Short Term Memory
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Department of Informatic and Software Engineering > Research papers
Depositing User: ePrints Depositor
Date Deposited: 19 Nov 2024 10:37
Last Modified: 19 Nov 2024 10:37
URI: https://eprints.cihanuniversity.edu.iq/id/eprint/2618

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