Improve feature extraction module scene text detection method
The characteristic extraction module based on deep learning is generally large network,which is generally large net-work with complex and low efficiency.In order to reduce the complexity of the text detection model and detect the text faster and effec-tively,on the basis of a divided gradient expansion network PSENet,use lightweight small network MobileNet V3 as a local feature ex-traction module to reduce the number of parameters,combined with multiple combination class convolution to extract the regional char-acteristics of irregular texts;use the optimizer Adam to calculate the adaptive learning rate of each parameter,accelerate the training optimization process,and improve the model operation efficiency.Verification on the data set ICDAR2015,the experimental results show that the improved algorithm has improved significantly in terms of performance.
Deep learningText detectionPSENetFeature extractionMobileNet V3
国家科技期刊平台
NSTL
万方数据
维普
