AS400-DET: Detection using Deep Learning Model for IBM i (AS/400)
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916981132754944 |
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| author | Tran, Thanh Luu, Son T. Bui, Quan Nomura, Shoshin |
| author_facet | Tran, Thanh Luu, Son T. Bui, Quan Nomura, Shoshin |
| contents | This paper proposes a method for automatic GUI component detection for the IBM i system (formerly and still more commonly known as AS/400). We introduce a human-annotated dataset consisting of 1,050 system screen images, in which 381 images are screenshots of IBM i system screens in Japanese. Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We then develop a detection system based on state-of-the-art deep learning models and evaluate different approaches using our dataset. The experimental results demonstrate the effectiveness of our dataset in constructing a system for component detection from GUI screens. By automatically detecting GUI components from the screen, AS400-DET has the potential to perform automated testing on systems that operate via GUI screens. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AS400-DET: Detection using Deep Learning Model for IBM i (AS/400) Tran, Thanh Luu, Son T. Bui, Quan Nomura, Shoshin Computer Vision and Pattern Recognition Artificial Intelligence This paper proposes a method for automatic GUI component detection for the IBM i system (formerly and still more commonly known as AS/400). We introduce a human-annotated dataset consisting of 1,050 system screen images, in which 381 images are screenshots of IBM i system screens in Japanese. Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We then develop a detection system based on state-of-the-art deep learning models and evaluate different approaches using our dataset. The experimental results demonstrate the effectiveness of our dataset in constructing a system for component detection from GUI screens. By automatically detecting GUI components from the screen, AS400-DET has the potential to perform automated testing on systems that operate via GUI screens. |
| title | AS400-DET: Detection using Deep Learning Model for IBM i (AS/400) |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.13032 |