Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts. In this research, we improve the design of CN (Vector version) namely we expand more Pooling layers to filter image backgrounds and increase Reconstruction layers to make better image restoration. Additionally, we perform experiments to compare accuracy and speed of CN versus DL models. In DL models, we utilize Inception V3 and DenseNet V201 for powerful computers besides NASNet, MobileNet V1 and MobileNet V2 for small and embedded devices. We evaluate our models on a fingerspelling alphabet dataset from American Sign Language (ASL). The results show that CNs perform comparably to DL models while dramatically reducing training time. We also make a demonstration and give a link for the purpose of illustration.

翻译：胶囊网络（CN）为深度学习（DL）领域提供了新的架构。尽管其有效性已在MNIST和smallNORB数据集上得到验证，但对于具有显著上下文差异的图像数据集，该网络仍面临挑战。本研究改进了胶囊网络（向量版本）的设计，具体扩展了更多池化层以过滤图像背景，并增加了重建层以提升图像复原效果。此外，我们通过实验对比了胶囊网络与深度学习模型在准确率和速度上的表现。在深度学习模型中，我们针对高性能计算机采用了Inception V3和DenseNet V201，针对小型和嵌入式设备则使用了NASNet、MobileNet V1和MobileNet V2。我们在美国手语（ASL）手指拼写字母数据集上评估了模型。结果表明，胶囊网络在训练时间大幅减少的同时，性能与深度学习模型相当。我们还制作了演示并提供链接以供说明。