Optical character recognition (OCR) algorithms often run slow. They may take several seconds to recognize the texts on a GUI screen, which makes OCR-based widget localization in test automation unfriendly for use, especially on GPU-free computers. This paper first concludes a common type of widget text to be located in GUI testing: label text, which are short texts in widgets like buttons, menu items, and window titles. We then investigate the characteristics of texts on a GUI screen and introduce a fast GPU-independent Label Text Screening (LTS) technique to accelerate the OCR process for label text localization. The technique opens the black box of OCR engines and uses a combination of simple methods to avoid excessive text analysis on a screen as much as possible. Experiments show that, on the subject datasets, LTS reduces the average OCR-based label text localization time to a large extent. On 4k resolution GUI screens, it keeps the localization time below 0.5 seconds for over about 60% of cases without GPU support on a normal laptop computer. In contrast, the existing CPU-based approaches built on popular OCR engines Tesseract, PaddleOCR, and EasyOCR usually need over 2 seconds to achieve the same goal on the same platform. Even with GPU acceleration, they can hardly keep the analysis time in 1 second. We believe the proposed approach would be helpful for implementing OCR-based test automation tools.

Ju Qian

Nanjing University of Aeronautics and Astronautics

Yingwei Ma

Nanjing University of Aeronautics and Astronautics

Chenghao Lin

Nanjing University of Aeronautics and Astronautics

Lin Chen

Nanjing University

China