Structured Text Extraction — Form & Table Extraction

A native PDF stores text as character objects with position coordinates, so extraction reads those objects directly without needing to recognise characters from pixels. A scanned or image-based PDF stores pages as raster images; extraction must first run an OCR engine to convert the image pixels into a text layer, and the quality of that layer determines the quality of everything extracted from it.

OCR quality directly controls extraction accuracy. A high character-error rate in the OCR output means cell values will contain substituted, missing, or spurious characters. Before running the extraction pipeline it is worth assessing OCR accuracy on a sample of pages; if error rates are high, improving the scan resolution or applying OCR post-correction is preferable to attempting extraction on noisy text.

Simple rule-based and whitespace-heuristic detectors often struggle with merged cells, rotated headers, and nested tables. Image-based deep-learning detection models, as benchmarked by Zhong et al. (2020), handle more complex layouts, but irregular structures always warrant manual spot-checking of the extracted output.

There is no minimum document count in the statistical sense because the method does not fit a model — it processes each document independently. A working floor of around ten documents is a practical baseline to verify that the pipeline behaves correctly across the format variants present in the corpus.