International Journal For Multidisciplinary Research

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Bangla Handwritten Character Recognition Using Deep Learning Approaches and its Explainability With AI

Author(s) Md. Mahedi Hasan, Fahim Habib, Md. Sharzul Mostafa
Country Bangladesh
Abstract The realm of Bangla handwritten character recognition (BHCR) has long been overshadowed by the dominance of more mainstream languages, despite Bangla's status as Deep learning (DL) approaches have led to substantial improvements in handwritten character recognition (BHCR) for Bangla, one of the humanity's frequently spoken languages. These methods generally an optimal fit for BHCR because they are good at selecting high-level characteristics from intricate information. In our comprehensive study, we meticulously explored the efficacy of twelve DL models on the arduous task of Bangla character recognition, meticulously evaluating their performance on two distinct datasets: a handwritten character dataset and CMATERDB [1], comprising a formidable collection of 15,000 images. Additionally, we provided an audit of the DL models' achievements for BHCR. Among the compared models are LSTM, Bi-LSTM, CNN, Inception, VGG, and ResNet. we achieved the maximum performance at ResNet152V2. In this study, One of the most exquisite identification rates for Bengali character recognition currently available has been demonstrated by the suggested technique, which displayed an adequate 98.76% recognition accuracy on the dataset.
Keywords Bangla handwritten character recognition (BHCR), Deep learning (DL), Comparative performance, LSTM, Bi-LSTM, CNN, Inception, VGG, ResNet, learning, Prediction
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-13
Cite This Bangla Handwritten Character Recognition Using Deep Learning Approaches and its Explainability With AI - Md. Mahedi Hasan, Fahim Habib, Md. Sharzul Mostafa - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.22574
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.22574
Short DOI https://doi.org/gtzjg6
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