Page Count Is a Poor Metric for Estimating PDF-to-Text Extraction Work

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A developer tested seven English documents through a PDF extraction tool to evaluate how well page count predicts post-processing effort, finding it to be a weak indicator. A single page from a 1734 book required more manual fixes than two pages of a modern academic paper, with the type of error mattering more than the quantity. The author categorized each error — called a 'spot' — into three buckets: scriptable fixes, human review required, or pages needing full re-OCR. A short script handling hyphen joins, tab-to-space conversion, and timestamp spacing resolved the majority of errors automatically. However, edge cases like conflicting OCR text layers and fused multi-column lines still required human judgment, underscoring that complexity, not page count, should drive project estimates.

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