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Text recognition in natural scene is a challenging problem due to the manyfactors affecting text appearance. In this paper, we presents a method thatdirectly transcribes scene text images to text without needing of sophisticatedcharacter segmentation. We leverage recent advances of deep neural networks tomodel the appearance of scene text images with temporal dynamics. Specifically,we integrates convolutional neural network (CNN) and recurrent neural network(RNN) which is motivated by observing the complementary modeling capabilitiesof the two models. The main contribution of this work is investigating howtemporal memory helps in an segmentation free fashion for this specificproblem. By using long short-term memory (LSTM) blocks as hidden units, ourmodel can retain long-term memory compared with HMMs which only maintainshort-term state dependences. We conduct experiments on Street View HouseNumber dataset containing highly variable number images. The resultsdemonstrate the superiority of the proposed method over traditional HMM basedmethods.

Memory Matters: Convolutional Recurrent Neural Network for Scene Text Recognition