Form-Based Test Modeling, Analysis, and Complexity Evaluation for Smart OCR Apps

OCR systems are increasingly being used in high-stakes and form-based applications, such as employment onboarding and voter registration, where accuracy is crucial to ensure system reliability. The paper introduces a test modeling and evaluation framework for Smart OCR applications that combines decision tables, context trees, and component-level complexity analysis to permit complete and automated validation. The proposed approach models document structure, environment (lighting, angle, and quality of images), and completeness of the input used to produce representative test cases of different operational situations. Accuracy and coverage measures are also used together to measure fidelity of recognition and structural completeness of multiple form components at the multimodal level, such as text fields, tables, checkboxes, radio buttons, and signatures. The framework is confirmed with an empirical case study of the application of employment and voter registration forms with three commercial OCR tools, such as Microsoft Lens, Amazon Textract, and Parsio.io. The experimental findings show the apparent trade-offs between accuracy and coverage and indicate considerable differences in symbolic and contextual extraction abilities by tools. Parsio.io scores the best on the criteria of balanced performance, as it has a high coverage and multimodal recognition strength among the tested systems.