Engineering Research & Scholarly Papers
Empirical investigations, software benchmarking, and system architecture studies. All papers are published under open access standards with HTML views and downloadable documents.
Empirical Evaluation of AI-Assisted Workflows in Web Application Development
A systematic study on code synthesis accuracy, type-safety enforcement, and developer cycle efficiency.
The integration of Large Language Models (LLMs) into modern web development pipelines presents significant opportunities for developer acceleration alongside novel challenges regarding code correctness, security boundaries, and runtime regressions. This paper explores the impact of strict type systems (TypeScript) acting as automated verification guardrails when paired with AI coding assistants. Through controlled benchmarks across common full-stack development scenarios, we analyze error rates, compilation pass percentages, and maintenance overhead.
High-Throughput Optical Character Recognition & Structural Data Ingestion
Benchmarking layout extraction accuracy and bounding-box normalization in heterogeneous document pipelines.
Extracting structured data from scanned and photographed physical documents remains a critical bottleneck in digital transformation workflows. This paper analyzes pipeline designs that combine image pre-processing (binarization, deskewing) with deep neural character recognition and post-processing semantic normalization. We propose an adaptive bounding-box cluster algorithm that enhances table structure recovery by 37% over baseline OCR implementations.