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ScanSSD: Scanning Single Shot Detector for Mathematical Formulas in PDF Document Images

arXiv 2003.08005 ↗First posted 18 Mar 2020cs.CVcited by 3

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Abstract (arXiv)

We introduce the Scanning Single Shot Detector (ScanSSD) for locating math formulas offset from text and embedded in textlines. ScanSSD uses only visual features for detection: no formatting or typesetting information such as layout, font, or character labels are employed. Given a 600 dpi document page image, a Single Shot Detector (SSD) locates formulas at multiple scales using sliding windows, after which candidate detections are pooled to obtain page-level results. For our experiments we use the TFD-ICDAR2019v2 dataset, a modification of the GTDB scanned math article collection. ScanSSD detects characters in formulas with high accuracy, obtaining a 0.926 f-score, and detects formulas with high recall overall. Detection errors are largely minor, such as splitting formulas at large whitespace gaps (e.g., for variable constraints) and merging formulas on adjacent textlines. Formula detection f-scores of 0.796 (IOU ≥0.5) and 0.733 (IOU ≥ 0.75) are obtained. Our data, evaluation tools, and code are publicly available.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2020.

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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.

3 papers.