Memory Matters: Convolutional Recurrent Neural Network for Scene Text Recognition
This addresses the problem of recognizing text in natural scenes for computer vision applications, representing an incremental improvement by combining existing CNN and RNN techniques.
The paper tackles scene text recognition by developing a convolutional recurrent neural network that directly transcribes images to text without character segmentation, demonstrating superiority over traditional HMM-based methods on the Street View House Number dataset.
Text recognition in natural scene is a challenging problem due to the many factors affecting text appearance. In this paper, we presents a method that directly transcribes scene text images to text without needing of sophisticated character segmentation. We leverage recent advances of deep neural networks to model 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 capabilities of the two models. The main contribution of this work is investigating how temporal memory helps in an segmentation free fashion for this specific problem. By using long short-term memory (LSTM) blocks as hidden units, our model can retain long-term memory compared with HMMs which only maintain short-term state dependences. We conduct experiments on Street View House Number dataset containing highly variable number images. The results demonstrate the superiority of the proposed method over traditional HMM based methods.