Handwritten character recognition is a crucial task because of its abundant
applications. The recognition task of Bangla handwritten characters is
especially challenging because of the cursive nature of Bangla characters and
the presence of compound characters with more than one way of writing. In this
paper, a classification model based on the ensembling of several Convolutional
Neural Networks (CNN), namely, BanglaNet is proposed to classify Bangla basic
characters, compound characters, numerals, and modifiers. Three different
models based on the idea of state-of-the-art CNN models like Inception, ResNet,
and DenseNet have been trained with both augmented and non-augmented inputs.
Finally, all these models are averaged or ensembled to get the finishing model.
Rigorous experimentation on three benchmark Bangla handwritten characters
datasets, namely, CMATERdb, BanglaLekha-Isolated, and Ekush has exhibited
significant recognition accuracies compared to some recent CNN-based research.
The top-1 recognition accuracies obtained are 98.40%, 97.65%, and 97.32%, and
the top-3 accuracies are 99.79%, 99.74%, and 99.56% for CMATERdb,
BanglaLekha-Isolated, and Ekush datasets respectively.

基于多个卷积神经网络的集成模型 BanglaNet 被提出用于分类孟加拉基本字符、复合字符、数字和修饰符。在多个数据集上进行的严格实验表明，相比于最近的基于卷积神经网络的研究，该模型获得了显著的识别准确率。