Abstract: Existing scene text recognition methods often face the problems of low robustness and poor generalization ability in the few-shot and language-independent scene.To solve this problem,on the one hand,a dual-stream network structure based on the fusion of space domain and frequency domain features is proposed in the feature extraction stage.It consists of a deep residual convolutional network branch for extracting spatial domain features,and a shallow neural network with one-dimensional fast fourier transform(FFT) branch for extracted frequency features.And then apply the channel attention mechanism to fuse the two features.On the other hand,in the sequence modeling stage,a multi-scale one-dimensional convolution module is proposed to replace the bidirectional long short-term memory(BiLSTM) according to the characteristics of the language-independent scene.Finally,a complete model is built by combining the existing TPS rectification module and CTC decoder.The transfer learning me-thod is adopted in the training process.Pre-training is performed on the large English datasets first,and then fine-tuning is performed on the target datasets.Experimental results on two few-shot language-independent datasets compiled in the paper show that the method is superior to the existing methods in terms of accuracy,which verifies that it has high robustness and generalization ability in this scenario.Moreover,the method using the feature extraction module described in the paper is better than the baseline on the five benchmark datasets of language-dependent scene(no fine-tuning),which verifies the effectiveness and versati-lity of the dual-stream feature fusion network proposed in the paper.

Key words: Deep learning, Scene text recognition, Dual-stream network, Frequency domain branch, Few-shot