DSAN: Double Supervised Network with Attention Mechanism for Scene Text Recognition In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel endtoend trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision comes from contextlevel modelling branch and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method achieves the current stateoftheart results on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.

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.