Natural scene text detection based on LFN

In the field of natural scene text detection, the existing deep learning network has the situation of text false detection, missed detection, and inaccurate positioning. To solve this problem, a text detection algorithm based on Large Receptive Field Feature Network (LFN) is designed. First, ShuffleNet V2 is selected as a lightweight backbone network with better speed and accuracy, and a fine-grained feature fusion module is added to obtain more hidden text feature information. Then the double fusion feature extraction module is constructed to extract multi-scale features from the input image by analyzing the different receptive fields of different scale feature maps and the influence of the size of the feature map obtained after normalization of feature maps with different scales on the results is compared, thereby reduced feature loss and increased the receptive field. Finally, Dice Loss is introduced into the differentiable binary module to deal with the imbalance between positive and negative samples and increase the accuracy of text location. Experiments result on the ICDAR2015 and CTW1500 datasets show that the network has significantly improved text detection in both performance and speed. The F1 on ICDAR2015 dataset is 86.1%, which is 0.4% higher than PSENet method with the best performance and the speed is 50.3fps, which is about 1.92 times higher than DBNet method with the fastest speed. The F1 on CTW1500 dataset is 83.2%, which is 1% higher than PSENet method and the speed is 35fps, which is about 1.65 times higher than EAST method.