Improving irregular text recognition by integrating gabor convolutional network

Scene text, especially irregular text, is difficult to recognize due to the arbitrary-oriented characters and irregular arrangement. Most existing methods address the irregular text by rectifying it into a regular one, which achieve good performance. However, these methods are possible to remove character information in some curved texts. To overcome this issue, we focus on extracting features that are robust to orientation changes instead of rectifying. In this work, we propose an end-to-end trainable model that combines a Gabor Convolutional Network (GCN) and a Sequence Recognition Network (SRN). The GCN is capable of extracting more robust features against the orientation, which is produced by incorporating Gabor filters of different orientations into Convolutional Neural Network (CNN). The SRN is an attention-based sequence-to-sequence model that sequentially outputs characters from the robust features. We evaluate the recognition accuracy of the proposed method on various benchmark datasets of scene text, including both regular and irregular texts. The extensive experimental results show that our proposed method achieves the state-of-the-art recognition performance on most of the irregular benchmarks as well as a regular benchmark.