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Table 2 Reviewed representations and descriptors for symbol recognition and spotting

From: Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

Paradigm*

Descriptor

Robustness and invariance

Features used

P

\(\mathcal {F}\)-signature [11]

Robust to geometric transformations and noise

Image pixels

P

Pixel-Level Constraint [12]

Scale and rotation invariant and robust to degradation

Image skeleton

P

Blurred Shape Model (BSM) [13, 14]

Robust to soft, rigid, and elastic deformations

Skeleton points

P

Circular Blurred Shape Model (CBSM) [15, 16],

BSM properties + rotation invariant

Contour map (flexible for other representations)

P

Shape Context Descriptor (SCIP) and extensions [20, 22]

Scale and rotation invariant

Interest points

V

Perceptual grouping, Fully Visibility Graph (FVG) [24]

Rotation and scale invariant

Vectorial primitives

V

Structural representation and Attributed Relational Graph (ARG) [25, 26, 29, 30]

Scale and rotation invariant, robust to small variations

Vectors and quadrilateral primitives

V

Hierarchical Plausibility Graph (HPG) [32, 33]

Robust to various distortions

Critical points and lines

V

Shape, topology, and Region Adjacency Graph (RAG) [35, 36]

Rotation and scale invariant

Image regions

V

Boundary and Region Adjacency Graph (RAG) [37]

Rotation and scale invariant

Image regions

V

Convexity and Near Convex Region Adjacency Graph (NCRAG) [38]

Rotation and scale invariant

Oriented line segments

V

Bag-of-GraphPaths (BoGP) [39]

Rotation invariant

Critical points

V

Jacobs’ statistical grouping [45]

Scale and rotation invariant

Contour map

V

Bag-of-Relations (BoR) [31]

Scale and rotation invariant and robust to irregularities

Thick (solid) components, circles, corners, and extremities

V

Cassinian ovals [47]

Not invariant to scaling and rotation

Polylines of closed region contours

  1. *P = pixel-based, V = vector-based
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