  Recurrent neural network (RNN) and connectionist temporal classification
(CTC) have showed successes in many sequence labeling tasks with the strong
ability of dealing with the problems where the alignment between the inputs and
the target labels is unknown. Residual network is a new structure of
convolutional neural network and works well in various computer vision tasks.
In this paper, we take advantage of the architectures mentioned above to create
a new network for handwritten digit string recognition. First we design a
residual network to extract features from input images, then we employ a RNN to
model the contextual information within feature sequences and predict
recognition results. At the top of this network, a standard CTC is applied to
calculate the loss and yield the final results. These three parts compose an
end-to-end trainable network. The proposed new architecture achieves the
highest performances on ORAND-CAR-A and ORAND-CAR-B with recognition rates
89.75% and 91.14%, respectively. In addition, the experiments on a generated
captcha dataset which has much longer string length show the potential of the
proposed network to handle long strings.
