论文标题
部分可观测时空混沌系统的无模型预测
Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition
论文作者
论文摘要
由于日常生活中的数字化，因此需要自动识别手写数字的需求正在增加。手写数字识别对于各个行业的众多应用都是必不可少的。孟加拉语是全球第五大语言，其中有2.65亿言语（本地和非本地人组合），全球4％的人口说孟加拉语。由于孟加拉语在形状，大小和写作风格方面的写作复杂性，研究人员使用监督的机器学习算法没有获得更好的准确性。此外，关于孟加拉手写数字识别（BHWDR）的研究更少。在本文中，我们提出了一种新型的基于CNN的预训练手写数字识别模型，其中包括Resnet-50，Inception-V3和ExcilityNetB0在NumtadB数据集上的17,000个实例的NumtadB数据集上具有10个类别。结果超过了10位数字的其他模型的表现，其表现超过了其他模型的表现。此外，我们通过其他研究评估了结果或模型，同时提出了未来的研究
Due to digitalization in everyday life, the need for automatically recognizing handwritten digits is increasing. Handwritten digit recognition is essential for numerous applications in various industries. Bengali ranks the fifth largest language in the world with 265 million speakers (Native and non-native combined) and 4 percent of the world population speaks Bengali. Due to the complexity of Bengali writing in terms of variety in shape, size, and writing style, researchers did not get better accuracy using Supervised machine learning algorithms to date. Moreover, fewer studies have been done on Bangla handwritten digit recognition (BHwDR). In this paper, we proposed a novel CNN-based pre-trained handwritten digit recognition model which includes Resnet-50, Inception-v3, and EfficientNetB0 on NumtaDB dataset of 17 thousand instances with 10 classes.. The Result outperformed the performance of other models to date with 97% accuracy in the 10-digit classes. Furthermore, we have evaluated the result or our model with other research studies while suggesting future study
