From Résumés(PDFs) to Clean Data: Layout-Aware Parsing with Tiny LLMs — AI Innovations and Insights 88

Have you ever wondered how to parse a résumé, or had to work with résumés on the job?

This article will give you some useful insights.

Why Traditional “OCR + LLM” Pipelines Fall Short for Resume Parsing

Building a practical resume analysis system at industrial scale faces three key challenges:

• Layout and Content Heterogeneity: Real-world resumes are highly diverse in both structure and content. Key details might be tucked inside images or scattered across complex, multi-column formats that disrupt the standard reading order. Furthermore, the vast diversity in linguistic styles also poses a challenge for consistent parsing. If the parser simply reads top to bottom, left to right, it often ends up misinterpreting the intended flow of information.

• High inference cost: Feeding messy, unstructured text directly into a large language model might work technically, but it’s slow and expensive. This approach isn’t viable when speed and scale matter, especially in real-time applications.

• Lack of Standardized Data and Evaluation Tools: Due to privacy concerns, high-quality annotated resume datasets are rare. Furthermore, evaluating extraction quality manually at scale is difficult, especially for list-style entities like work experience. Therefore, without automated and reliable evaluation frameworks, optimization becomes guesswork.

A Three-Stage Pipeline for Layout-Aware Resume Parsing