BanglaNet：基于卷积神经网络集成的孟加拉语手写字符识别
BanglaNet: Bangla Handwritten Character Recognition using Ensembling of Convolutional Neural Network
AI总结：
针对孟加拉语手写字符识别难题，提出BanglaNet模型，集成Inception、ResNet和DenseNet三种CNN，在三个基准数据集上取得最高98.40%的top-1准确率。
摘要：
手写字符识别因其广泛的应用而成为一项关键任务。由于孟加拉语字符的连笔特性以及存在多种书写方式的复合字符，孟加拉语手写字符的识别任务尤其具有挑战性。本文提出了一种基于多个卷积神经网络（CNN）集成的分类模型，即BanglaNet，用于对孟加拉语基本字符、复合字符、数字和修饰符进行分类。基于Inception、ResNet和DenseNet等先进CNN模型的思想，训练了三个不同的模型，分别使用增强和未增强的输入。最后，所有这些模型被平均或集成以获得最终模型。在三个基准孟加拉语手写字符数据集（即CMATERdb、BanglaLekha-Isolated和Ekush）上的严格实验表明，与近期一些基于CNN的研究相比，取得了显著的识别准确率。在CMATERdb、BanglaLekha-Isolated和Ekush数据集上，top-1识别准确率分别为98.40%、97.65%和97.32%，top-3准确率分别为99.79%、99.74%和99.56%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
