# Computer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR

arXiv: https://arxiv.org/abs/2410.03536 · HTML: https://mlsanity.com/paper/2410.03536/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [Metamorphic Testing: A New Approach for Generating Next Test Cases](https://mlsanity.com/paper/2002.12543/) (2020)
- [Quality Assurance for AI-based Systems: Overview and Challenges](https://mlsanity.com/paper/2102.05351/) (2021)
- [AEON: A Method for Automatic Evaluation of NLP Test Cases](https://mlsanity.com/paper/2205.06439/) (2022)
- [DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts](https://mlsanity.com/paper/2202.01414/) (2022)
- [Distribution Awareness for AI System Testing](https://mlsanity.com/paper/2105.02540/) (2021)
- [DISGO: Automatic End-to-End Evaluation for Scene Text OCR](https://mlsanity.com/paper/2308.13173/) (2023)
- [End-To-End Measure for Text Recognition](https://mlsanity.com/paper/1908.09584/) (2019)

## Built on by (most cited first, 2 of 2)

- [The Character Error Vector: Decomposable errors for page-level OCR evaluation](https://mlsanity.com/paper/2604.06160/) (2026)
- [Operational Robustness of LLMs on Code Generation](https://mlsanity.com/paper/2602.18800/) (2026)

## Abstract (arXiv)

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.