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Recurrent neural network (RNN) and connectionist temporal classification(CTC) have showed successes in many sequence labeling tasks with the strongability of dealing with the problems where the alignment between the inputs andthe target labels is unknown. Residual network is a new structure ofconvolutional neural network and works well in various computer vision tasks.In this paper, we take advantage of the architectures mentioned above to createa new network for handwritten digit string recognition. First we design aresidual network to extract features from input images, then we employ a RNN tomodel the contextual information within feature sequences and predictrecognition results. At the top of this network, a standard CTC is applied tocalculate the loss and yield the final results. These three parts compose anend-to-end trainable network. The proposed new architecture achieves thehighest performances on ORAND-CAR-A and ORAND-CAR-B with recognition rates89.75% and 91.14%, respectively. In addition, the experiments on a generatedcaptcha dataset which has much longer string length show the potential of theproposed network to handle long strings.

Handwritten digit string recognition by combination of residual network and RNN-CTC