Ionospheric data recognition and form recovery based on deep learning
To address the problems of low clarity and character similarity of historical data of ionospheric prints,an ionospheric data detection and recognition method based on DBNET and CRNN is proposed.The noise interference is removed through image preprocessing,and DBNET is used to locate the text area of the target image.The convolutional layer in CRNN is added,and the window size of the two pooling layers is changed to 2 × 1 to extract the text detected by feature recognition with a larger width,and batch normaliza-tion processing is added to accelerate training.RARE is used to complete the form recovery of identification results.Experiment results show that this method has a good recognition effect on ionospheric printed body data,and the correct recognition rate of characters reaches more than 98％.
deep learningprinted datacharacter detectioncharacter recognitionForm recovery
NETL
NSTL
万方数据
