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Deep Learning Based Map Code Recognition Method by Yikun Zhu; Congzhou Guo; Dali Wang; Xiaochong Tong; He Li; Hui Yuan is a scholarly article available to read on EtoBox.

What is Deep Learning Based Map Code Recognition Method about?

For the efficient digitalization of maps, it is necessary to register map code, which indicates scale and area range. The manual register of the map code is time-consuming and laborious. Moreover, the recognition accuracy of the traditional text detection method based on template matching cannot be guaranteed. In this study, we propose a deep learning method to recognize image code and solve the problem of inaccurate detection and inaccurate positioning of text areas in map code recognition. A map code target recognition neural network referred to as YOLO-MCRNN is trained first to identify, crop, and binarize the code area and obtain detailed images. Architecturally, an attention mechanism is added to a bidirectional long short-term memory structure and integrated into the map code target recognition network. Single numbers were cut according to variations in pixel value to establish a template library used in the matching method to recognize the code. Then, we conducted a comparative experiment to demonstrate the efficacy of the proposed deep learning method, and the results show that the detection network model improved on the performance of the existing state-of-the-art methods.

Author

Yikun Zhu; Congzhou Guo; Dali Wang; Xiaochong Tong; He Li; Hui Yuan

Publisher

IEEE

Published

2022

Language

EN