  This study compares sequential image classification methods based on
recurrent neural networks. We describe methods based on recurrent neural
networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term
memory(BiLSTM) architectures, etc. We also review the state-of-the-art
sequential image classification architectures. We mainly focus on LSTM, BiLSTM,
temporal convolution network, and independent recurrent neural network
architecture in the study. It is known that RNN lacks in learning long-term
dependencies in the input sequence. We use a simple feature construction method
using orthogonal Ramanujan periodic transform on the input sequence.
Experiments demonstrate that if these features are given to LSTM or BiLSTM
networks, the performance increases drastically.
  Our focus in this study is to increase the training accuracy simultaneously
reducing the training time for the LSTM and BiLSTM architecture, but not on
pushing the state-of-the-art results, so we use simple LSTM/BiLSTM
architecture. We compare sequential input with the constructed feature as input
to single layer LSTM and BiLSTM network for MNIST and CIFAR datasets. We
observe that sequential input to the LSTM network with 128 hidden unit training
for five epochs results in training accuracy of 33% whereas constructed
features as input to the same LSTM network results in training accuracy of 90%
with 1/3 lesser time.
