Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluation
Researchers evaluated three methods for extracting information from tabular PDF documents, using academic course registration forms as a case study. The strategies included using only large language models (LLMs), a hybrid approach combining deterministic methods with LLMs, and a pipeline using Camelot with an LLM fallback. Experiments showed that the hybrid approach improved efficiency for metadata extraction, while the Camelot pipeline with LLM fallback achieved the highest accuracy and computational efficiency, performing extraction in under a second per document. AI
IMPACT Demonstrates efficient and accurate methods for extracting structured data from complex PDF documents, potentially aiding research and data processing in computationally constrained environments.
