An Efficient Approach to Creating Hand-Drawn Dataset for UI Manuscript Recognition

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An Efficient Approach to Creating Hand-Drawn Dataset for UI Manuscript Recognition

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[1]YANG Qi,LIU Mugeng,MA Yun.An Efficient Approach to Creating Hand-Drawn Dataset for UI Manuscript Recognition[J].Journal of Zhengzhou University (Engineering Science),2022,43(06):1-7.[doi:10.13705/j.issn.1671-6833.2022.06.009]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 06 Page number: 1-7 Column: Public date: 2022-09-02

Title:: An Efficient Approach to Creating Hand-Drawn Dataset for UI Manuscript Recognition

Author(s):: YANG Qi¹; LIU Mugeng²; MA Yun³; 1.School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen 518055, China;
2.Department of Computer Science, Peking University, Beijing 100871, China;
3. Institute for Artificial Intelligence, Peking University, Beijing 100871, China

Keywords:: intelligent software developing service; UI manuscript; recognition; object detection; dataset; data enhancement

CLC:: TP311；O244

DOI:: 10.13705/j.issn.1671-6833.2022.06.009

Abstract:: UI manuscript recognition is one of the important applications of image object detection in the area of software engineering. Due to the significant difference between UI manuscript images and natural images where UI manuscript images usually need to be drawn manually, it is difficult to build UI manuscript dataset for deep learning because of the dependency on tremendous manual efforts. To address the issue, in this study an approach called UIsketcher was proposed to efficiently generate UI manuscript dataset based on optimizing the current workflow. In UIsketcher, users should just draw some basic elements without labeling, and then the dataset could be automatically generated for training deep learning model. According to the experiment with UIsketcher, only 25% drawing workload of the traditional methods could get the similar training results. If the workload was 75%, the final accuracy was even better than that of traditional methods.

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Last Update: 2022-10-03