In this paper, we present a novel SpaTial Attention Residue Network (STAR-Net) for recognising scene texts. Our STAR-Net is equipped with a spatial attention mechanism which employs a spatial transformer to remove the distortions of texts in natural images. This allows the subsequent feature extractor to focus on the rectified text region without being sidetracked by the distortions. Our STAR-Net also exploits residue convolutional blocks to build a very deep feature extractor, which is essential to the successful extraction of discriminative text features for this fine grained recognition task. Combining the spatial attention mechanism with the residue convolutional blocks, our STAR-Net is the deepest end-to-end trainable neural network for scene text recognition. Experiments have been conducted on five public benchmark datasets. Experimental results show that our STAR-Net can achieve a performance comparable to state-of-the-art methods for scene texts with little distortions, and outperform these methods for scene texts with considerable distortions.
Liu et al. REF presented a similar method, named the Spatial Attention Residue Network (STAR-Net), for the unconstrained text recognition, which automatically rectifies distorted texts.
STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition
BMVC
Computer Science