Sharing My First AI Project to Solve Real-World Problem
Analysis
This article describes an open-source project, DART (Digital Accessibility Remediation Tool), aimed at converting inaccessible documents (PDFs, scans, etc.) into accessible HTML. The project addresses the impending removal of non-accessible content by large institutions. The core challenges involve deterministic and auditable outputs, prioritizing semantic structure over surface text, avoiding hallucination, and leveraging rule-based + ML hybrids. The author seeks feedback on architectural boundaries, model choices for structure extraction, and potential failure modes. The project offers a valuable learning experience for those interested in ML with real-world implications.
Key Takeaways
- •The project focuses on a practical problem: making documents accessible.
- •It highlights the importance of deterministic and auditable AI in real-world applications.
- •The project uses a hybrid approach, combining rule-based systems and ML, which is a common and effective strategy.
“The real constraint that drives the design: By Spring 2026, large institutions are preparing to archive or remove non-accessible content rather than remediate it at scale.”