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Handwritten Bangla character recognition using convolutional neural networks: a comparative study and new lightweight model

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Abstract

Handwriting is a crucial way to enhance character recognition and learn new words. However, the Bangla characters consist of very complex shapes and similar patterns. Deep learning (DL) techniques have become a prominent solution for handwritten Bangla character recognition (HBCR) due to their ability to extract high-level features from complex data. Several DL techniques have been proposed for HBCR, but they are computationally expensive and large in model size and thus not suitable for use in resource-constrained devices such as smartphones. In this study, we have evaluated the state-of-the-art DL models for HBCR. For this, we have used four existing datasets and created a merged dataset (by combining the four) for cross-dataset evaluation. We have provided a comparative performance analysis of the state-of-the-art DL models for HBCR. Additionally, we have proposed a new lightweight DL model for HBCR and evaluated its performance. The proposed DL model consists of 74 layers, including sub-layers, and its architecture is divided into five similar blocks. It includes the convolutional layers of (3, 3) and (5, 5) kernels, (1,1) stride, and the maximum pool layer of the (2, 2) pool size. The proposed model achieved accuracy, model size, loading and testing times of 96.87%, 13 MB, 9.11 s, and 7.95 s, respectively. The experimental results show that our model outperformed state-of-the-art models in terms of efficiency (loading and testing time) and model size with competitive accuracy.

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Data availability statements

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

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Author notes

  1. Md. Ekramul Hossainm and Muhammad Ashad Kabir have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chittagong University of Engineering and Technology (CUET), Chattogram, 4349, Bangladesh

    Md. Nahidul Islam Opu

  2. School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia

    Md. Ekramul Hossain & Muhammad Ashad Kabir

  3. Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW, 2008, Australia

    Md. Ekramul Hossain

Authors
  1. Md. Nahidul Islam Opu
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  2. Md. Ekramul Hossain
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  3. Muhammad Ashad Kabir
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Correspondence to Md. Ekramul Hossain.

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Opu, M.N.I., Hossain, M.E. & Kabir, M.A. Handwritten Bangla character recognition using convolutional neural networks: a comparative study and new lightweight model. Neural Comput & Applic 36, 337–348 (2024). https://doi.org/10.1007/s00521-023-09008-8

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