Natural Scene Text Detection Algorithm Combining Multi-granularity Feature Fusion

Abstract

In natural scenes,text information usually has the characteristics of diversity and complexity.Due to the way of manua-lly designing features,traditional natural scene text detection methods lack robustness,and the existing text detection methods based on deep learning have the problem of losing important feature information in the process of extracting features in each layer of the network.This paper proposes a natural scene text detection method combined with multi-granularity feature fusion.The main contribution of this method is that by combining the features of different granularities in the general feature extraction network and adding the residual channel attention mechanism,the model can pay more attention to the target feature information and suppress useless information on the basis of fully learning the feature information of different granularities in the image,and this method improves the robustness and accuracy of the model.The experimental results show that,compared with other latest me-thods,the model has achieved 85.3% accuracy and 82.53% F-value on public datasets,and has better performance.

Keywords

• feature extraction|multi-granularity information|residual attention|convolutional neural network