Abstract
Bangla language consists of fifty distinct characters and many compound characters. Several notable studies have been performed to recognize Bangla characters, both handwritten and optical. Our approach uses transfer learning to classify the basic, distinct, as well as compound Bangla handwritten characters while avoiding the vanishing gradient problem. Deep Neural Network techniques such as 3D Convolutional Neural Network (3DCNN), Residual Neural Network (ResNet), and MobileNet are applied to generate an end-to-end classification of all possible standard formations of handwritten characters in the Bangla language. The Bangla Lekha Isolated dataset, which contains 166,105 Bangla character image samples categorized into 84 distinct classes, is used for this classification model. The model achieved 99.82% accuracy on training data and 99.46% accuracy on test data. Comparisons with various state-of-the-art benchmarks of Bangla handwritten character classification show that the proposed model achieves better accuracy in classifying the data.
Abstract (translated)
孟加拉语由五十个不同的字符和许多复合字符组成。已经进行了多项研究来识别手写和光学形式的孟加拉语字符。我们的方法利用迁移学习对基本、独立以及复合孟加拉语手写字符进行分类，同时避免梯度消失问题。通过应用深度神经网络技术（如三维卷积神经网络(3DCNN)、残差神经网络(ResNet)和MobileNet）来生成孟加拉语言所有可能标准的手写字符形式的端到端分类。我们使用了Bangla Lekha Isolated数据集，该数据集中包含了166,105个归类为84种不同类别的孟加拉语字符图像样本。我们的模型在训练数据上达到了99.82%的准确率，在测试数据上则达到了99.46%的准确率。与各种最先进的孟加拉语手写字符分类基准进行比较，所提出的模型在分类数据时取得了更高的准确性。
URL
https://arxiv.org/abs/2509.03061
