  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.
