Open Access

ARTICLE

Development of a Lightweight Model for Handwritten Dataset Recognition: Bangladeshi City Names in Bangla Script

Md. Mahbubur Rahman Tusher¹, Fahmid Al Farid^2,*, Md. Al-Hasan¹, Abu Saleh Musa Miah¹, Susmita Roy Rinky¹, Mehedi Hasan Jim¹, Sarina Mansor², Md. Abdur Rahim³, Hezerul Abdul Karim^2,*

1 Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology (BAUST), Saidpur, 5310, Bangladesh
2 Faculty of Engineering, Multimedia University, Cyberjaya, 63100, Malaysia
3 Pabna University of Science and Technology, Pabna, 6600, Bangladesh

* Corresponding Authors: Fahmid Al Farid. Email: ; Hezerul Abdul Karim. Email:

Computers, Materials & Continua 2024, 80(2), 2633-2656. https://doi.org/10.32604/cmc.2024.049296

Received 02 January 2024; Accepted 30 April 2024; Issue published 15 August 2024

Abstract

The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting. The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems, particularly in critical areas such as postal automation and document processing. Notably, no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition. To bridge this gap, the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition. The emphasis on practical data for system training enhances accuracy. The research further conducts a comparative analysis, pitting state-of-the-art (SOTA) deep learning models, including EfficientNetB0, VGG16, ResNet50, DenseNet201, InceptionV3, and Xception, against a custom Convolutional Neural Networks (CNN) model named “Our CNN.” The results showcase the superior performance of “Our CNN,” with a test accuracy of 99.97% and an outstanding F1 score of 99.95%. These metrics underscore its potential for automating city name recognition, particularly in postal services. The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures. It encourages future research avenues, including dataset expansion, algorithm refinement, exploration of recurrent neural networks and attention mechanisms, real-world deployment of models, and extension to other regional languages and scripts. These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.

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