Attention After Attention: Reading Text in the Wild with Cross Attention Recent methods mostly regarded scene text recognition as a sequencetosequence problem. These methods roughly transform the image into a feature sequence and use the algorithms for sequencetosequence problem like CTC or attention to decode the characters. However, text in images is distributed in a twodimensional (2D) space and roughly converting the features of text into a feature sequence may introduce extra noise, especially if the text is irregular. In this paper, we propose a novel framework named cross attention network, which learns to attend to local features of a 2D feature map corresponding to individual characters. The network contains two 1D attention networks, which operates harmoniously in two directions. Thus, one of the attention modules vertically attends to the features corresponding to the whole text of 2D features and the other horizontal module selects the local features to decode individual characters. Extensive experiments are performed on various regular benchmarks, including SVT, ICDAR2003, ICDAR2013, and IIIT5KWords, which demonstrate that the proposed model either outperforms or is comparable to all previous methods. Moreover, the model is evaluated on irregular benchmarks including SVTPerspective, CUTE80 and ICDAR 2015. The performance on irregular benchmarks shows the robustness of our model.

A Comparative Study of AttentionBased EncoderDecoder Approaches to Natural Scene Text Recognition Attentionbased encoderdecoder approaches have shown promising results in scene text recognition. In the literature, models with different encoders, decoders and attention mechanisms have been proposed and compared on isolated word recognition tasks, where the models are trained on either synthetic word images or a small set of realworld images. In this paper, we investigate different components of the attention based framework and compare its performance with a CNNDBLSTMCTC based approach on largescale realworld scene text sentence recognition tasks. We train character models by using more than 1.6M realworld text lines and compare their performance on test sets collected from a variety of realworld scenarios. Our results show that (1) attention on a twodimensional feature map can yield better performance than onedimensional one and an RNN based decoder performs better than CNN based one; (2) attentionbased approaches can achieve higher recognition accuracy than CNNDBLSTMCTC based approaches on isolated word recognition tasks, but perform worse on sentence recognition tasks; (3) it is more effective and efficient for CNNDBLSTMCTC based approaches to leverage an explicit language model to boost recognition accuracy.