Spotting Symbols in Line Drawing Images Using Graph Representations

Abstract

Many methods of graphics recognition have been developed throughout the years for the recognition of pre-segmented graphics symbols but very few techniques achieved the objective of symbol spotting and recognition together in a generic case. To go one step forward through this objective, this paper presents an original solution for symbol spotting using a graph representation of graphical documents. The proposed strategy has two main step. In the first step, a graph based representation of a document image is generated that includes selection of description primitives (nodes of the graph) and organisation of these features (edges). In the second step the graph is used to spot interesting parts of the image that potentially correspond to symbols. The sub-graphs associated to selected zones are then submitted to a graph matching algorithm in order to take the final decision and to recognize the class of the symbol. The experimental results obtained on different types of documents demonstrates that the system can handle different types of images without any modification.

New trends on digitisation of complex engineering drawings

“…Similarly, Wenyin et al [118] made use of attributed graphs to represent graphics, where vertices represent the lines that compose the symbol and edges denote the kind of interaction between vectors. Furthermore, an advantage obtained from graphs as feature representations is the Statistical-based Bitmap [21,123] Pixel intensity [24] Others [49] Structural based Line vectors [15,52,53,83,99,118,120] Geometrical primitives [31,32,34,41,48,56,93,129,132] Contour [7,126] Moments [67] Others [2] Hybrid [75] capability of refining the features for a class of symbols. Such is the case presented by Jiang et al [63], where the prototype symbol of a class was calculated from a set of distorted symbols by extracting the features of all symbols, representing them as graphs, and applying a genetic algorithm to find the median graph.…”

Product graph-based higher order contextual similarities for inexact subgraph matching

“…To have a comparison among the state-of-the-art symbol spotting methods, we considered five different methods. They are: (1) Symbol spotting with graph representation (SSGR) [64], (2) Integer linear programming (ILPIso) [21], (3) Fuzzy graph embedding (FGE) [65], (4) Serialized graphs (SG) [8], (5) Near convex region adjacency graph (NCRAG) [66]. The quantitative results obtained by different methods are listed in Table 1 and the precision-recall curves are shown in Figure 9a.…”

Robust Symbol Localization Based on Junction Features and Efficient Geometry Consistency Checking

“…Because the number of detected keypoints would be very large and the local scale computed at each keypoint could be far from satisfaction, the ROI extraction step is thus fragile and time-consuming. Triggering mechanisms have been also developed from graphbased representations, as in [12], [13]. These proposed systems work from the ARGs, where the structures and attributes of the graphs are exploited to identify the ROIs.…”

“…Symbol localization can be defined as the ability of a system to localize the symbol entities in the complete documents. It could be embedded in the recognition/spotting method or works as a separated stage in a two-step system [12]. The approaches used for localization are similar for recognition and spotting.…”

“…To deal with the error-prone introduced in the vectorization process, the ARGs are extracted from low resolution images and processed by a contraction step. The authors in [12] apply a scoring process in graphs to look for specific attributes of nodes (e.g., small and perpendicular segments). Scores are propagated through the loops of shortest length in the graph.…”