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A Combinatorial Solution to Point Symbol Recognition.

Yining Quan^1,2, Yuanyuan Shi^3,4, Qiguang Miao^5,6

¹The School of Computer and Technology, Xidian University, Xi'an 710071, China. ynquan@xidian.edu.cn.

Sensors (Basel, Switzerland)

|October 14, 2018

Summary

This summary is machine-generated.

This study introduces a deep transfer learning approach for accurate point symbol recognition in map digitization. The method enhances generalization and improves simultaneous identification of multiple symbols.

Keywords:

deep transfer training feature extraction point symbols recognition preprocessing

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Area of Science:

Computer Science
Geographic Information Systems
Artificial Intelligence

Background:

Point symbol recognition is crucial for map digitization.
Existing methods struggle with multiple symbols and low accuracy.
A need exists for robust and accurate symbol identification systems.

Purpose of the Study:

To propose a deep transfer learning architecture for improved point symbol recognition.
To address the challenge of small datasets in symbol classification.
To enhance the accuracy and capability of identifying multiple symbols simultaneously.

Main Methods:

Developed a deep transfer learning architecture utilizing AlexNet.
Employed transfer learning by pretraining the model with the MNIST dataset.
Implemented a preprocessing step for coarse screening of suspected point symbol areas.

Main Results:

Achieved significant improvement in point symbol recognition accuracy.
Demonstrated high performance in simultaneously handling numerous symbol categories.
Enhanced model generalization despite limited training data.

Conclusions:

The proposed deep transfer learning method effectively addresses limitations in current point symbol recognition.
Pretraining with MNIST significantly boosts model performance and generalization.
The approach offers a robust solution for accurate and simultaneous multi-category symbol identification in map digitization.