ScanSSD: Scanning Single Shot Detector for Mathematical Formulas in PDF Document Images
This addresses the challenge of accurately locating math formulas in scanned documents for applications like document analysis and accessibility, representing an incremental improvement with specific gains.
The paper tackles the problem of detecting mathematical formulas in PDF document images using only visual features, achieving a formula detection f-score of 0.796 at IOU ≥0.5 and 0.733 at IOU ≥0.75.
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 $\geq0.5$) and 0.733 (IOU $\ge 0.75$) are obtained. Our data, evaluation tools, and code are publicly available.