Automatic Extraction Technology of Ionospheric Vertical Data with Handwritten Font
Guichang Su, Ruikun Zhang, Xiangpeng Liu · 2024
To solve the problems such as large size, dense text area and various handwriting styles, an automatic data extraction technology based on DB and CRNN algorithms is proposed, which mainly includes four modules: image preprocessing, form text detection, text recognition and layout processing. Firstly, contour extraction and tilt correction are used to preprocess the scanned images of handwriting vertical data. Then, Hough transform is used for table detection and DB algorithm is used for text detection. The image segmentation function is added before DB detection algorithm to improve detection accuracy. Finally, CRNN algorithm is used for text recognition, and coordinate fusion algorithm is used to save the recognition results into Excel standardized format to realize automatic data extraction and saving. The experimental results show that the text detection recall rate of the proposed algorithm is 96.77%, and the F-value of the comprehensive evaluation index of text recognition is 95.2%. Compared with other algorithms, its effectiveness is verified. Therefore, the automatic data extraction technology proposed in this paper has high practicability and can meet the actual needs of engineering applications.