Efficient Design and Compression of CNN Models for
Rapid Character Recognition
Dublin Core
Title
Efficient Design and Compression of CNN Models for
Rapid Character Recognition
Rapid Character Recognition
Subject
Lightweight CNN; model optimization; efficient deep learning; character recognition
Description
Convolutional Neural Networks (CNNs) are extensively utilized for image processing and recognition tasks; however, they often encounter challenges related to large model sizes and prolonged training times. These limitations
present difficulties in resource-constrained environments that require rapid model deployment and efficient computation. This study introduces a systematic approach to designing lightweight CNN models specifically for character recognition, emphasizing the reduction of model complexity, training duration, and computational costs
without sacrificing performance.
present difficulties in resource-constrained environments that require rapid model deployment and efficient computation. This study introduces a systematic approach to designing lightweight CNN models specifically for character recognition, emphasizing the reduction of model complexity, training duration, and computational costs
without sacrificing performance.
Creator
Onesinus Saut Parulian
Source
http://dx.doi.org/10.21609/jiki.v18i1.1443
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2025-02-08
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Onesinus Saut Parulian, “Efficient Design and Compression of CNN Models for
Rapid Character Recognition,” Repository Horizon University Indonesia, accessed October 30, 2025, https://repository.horizon.ac.id/items/show/8950.
Rapid Character Recognition,” Repository Horizon University Indonesia, accessed October 30, 2025, https://repository.horizon.ac.id/items/show/8950.