Resumen
One of a map?s fundamental elements is its annotations, and extracting these annotations is an important step in enabling machine intelligence to understand scanned map data. Due to the complexity of the characters and lines, extracting annotations from scanned Chinese maps is difficult, and there is currently little research in this area. A deep-learning-based framework for extracting annotations from scanned Chinese maps is presented in the paper. Improved the EAST annotation detection model and CRNN annotation recognition model based on transfer learning make up the two primary parts of this framework. Several sets of the comparative tests for annotation detection and recognition were created in order to assess the efficacy of this method for extracting annotations from scanned Chinese maps. The experimental findings show the following: (i) The suggested annotation detection approach in this study revealed precision, recall, and h-mean values of 0.8990, 0.8389, and 0.8635, respectively. These measures demonstrate improvements over the currently popular models of -0.0354 to 0.0907, 0.0131 to 0.2735, and 0.0467 to 0.1919, respectively. (ii) The proposed annotation recognition method in this study revealed precision, recall, and h-mean values of 0.9320, 0.8956, and 0.9134, respectively. These measurements demonstrate improvements over the currently popular models of 0.0294 to 0.1049, 0.0498 to 0.1975, and 0.0402 to 0.1582, respectively.