Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917794228994048 |
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| author | Shu, Jing Miu, Bing-Jiun Chang, Eugene Gao, Jerry Liu, Jun |
| author_facet | Shu, Jing Miu, Bing-Jiun Chang, Eugene Gao, Jerry Liu, Jun |
| contents | AI-based systems possess distinctive characteristics and introduce challenges in quality evaluation at the same time. Consequently, ensuring and validating AI software quality is of critical importance. In this paper, we present an effective AI software functional testing model to address this challenge. Specifically, we first present a comprehensive literature review of previous work, covering key facets of AI software testing processes. We then introduce a 3D classification model to systematically evaluate the image-based text extraction AI function, as well as test coverage criteria and complexity. To evaluate the performance of our proposed AI software quality test, we propose four evaluation metrics to cover different aspects. Finally, based on the proposed framework and defined metrics, a mobile Optical Character Recognition (OCR) case study is presented to demonstrate the framework's effectiveness and capability in assessing AI function quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03536 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR Shu, Jing Miu, Bing-Jiun Chang, Eugene Gao, Jerry Liu, Jun Software Engineering Artificial Intelligence Computer Vision and Pattern Recognition AI-based systems possess distinctive characteristics and introduce challenges in quality evaluation at the same time. Consequently, ensuring and validating AI software quality is of critical importance. In this paper, we present an effective AI software functional testing model to address this challenge. Specifically, we first present a comprehensive literature review of previous work, covering key facets of AI software testing processes. We then introduce a 3D classification model to systematically evaluate the image-based text extraction AI function, as well as test coverage criteria and complexity. To evaluate the performance of our proposed AI software quality test, we propose four evaluation metrics to cover different aspects. Finally, based on the proposed framework and defined metrics, a mobile Optical Character Recognition (OCR) case study is presented to demonstrate the framework's effectiveness and capability in assessing AI function quality. |
| title | Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR |
| topic | Software Engineering Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.03536 |