构建智能OCR测试框架，系统评估图像文字识别质量。
Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
- 提出三维分类模型评估图像文本提取功能
- 设计四类指标覆盖测试覆盖率与复杂度
- 在移动端OCR案例中验证框架有效性
基于AI的系统具有独特特性，同时也带来了质量评估挑战。因此，确保和验证AI软件质量至关重要。本文提出一种有效的AI软件功能测试模型以应对这一挑战。首先，对以往工作进行综合性文献综述，涵盖AI软件测试流程的关键方面。随后，引入一个三维分类模型，系统评估基于图像的文字提取AI功能，以及测试覆盖度和复杂度。为评估所提AI软件质量测试的性能，设计了四类评价指标，覆盖不同维度。最后，基于该框架和定义的指标，通过移动端光学字符识别（OCR）案例研究，展示了框架在评估AI功能质量方面的有效性和能力。
原文摘要 · Abstract (English)
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.
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