Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
This work addresses the problem of quality validation for AI-based systems, specifically in smart OCR, but it appears incremental as it builds on existing testing processes.
The authors tackled the challenge of evaluating AI software quality by proposing a functional testing model, which they demonstrated through a mobile OCR case study to assess image-based text extraction functions effectively.
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.