Statistical Grouping for Segmenting Symbols Parts from Line Drawings, with Application to Symbol Spotting

In this work, we describe the use of statistical grouping for partitioning line drawings into shapes, those shapes represent meaningful parts of the symbols that constitute the line drawings. This grouping method converts a complete line drawing into a set of isolated shapes. This conversion has two effects: (1) making isolated recognition methods applicable for spotting symbols in context, (2) identifying potential regions of interest for symbol spotting methods, hence making them perform faster and more accurately. Our grouping is based on finding salient convex groups of geometric primitives, followed by combining certain found convex groups together. Additionally, we show how such grouping can be used for symbol spotting. When applied on a dataset of architectural line drawings the grouping method achieved above 98.8% recall and 97.3% precision for finding symbols parts. Using the grouping information, the spotting method achieved 99.3% recall and 99.9% precision. Compared to the performance of the same method without grouping information, an overall speed-up factor of 3.2 is achieved with the same -or better -recall and precision values.