Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Proceedings - 6th IEEE International Conference on Artificial Intelligence Testing, AITest 2024
DOI
10.1109/AITest62860.2024.00011
First Page
21
Last Page
28
Abstract
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.
Keywords
AI software testing, Artificial Intelligence, computer vision, optical character recognition, quality assurance
Department
Computer Engineering
Recommended Citation
Jing Shu, Bing Jiun Miu, Eugene Chang, Jerry Gao, and Jun Liu. "Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR" Proceedings - 6th IEEE International Conference on Artificial Intelligence Testing, AITest 2024 (2024): 21-28. https://doi.org/10.1109/AITest62860.2024.00011
