  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.
