An improved example-driven symbol recognition approach in engineering drawings

In this paper, an improved example-driven symbol recognition algorithm is proposed for CAD engineering drawings. First, in order to represent the structure of symbols more clearly and simply, we involve the text entity as one of the basic elements and redefine the relation representation mechanism, which is the foundation for the following algorithms. Then, the structure graph and a constrained tree can be established automatically for the target symbol, using the knowledge acquisition algorithm. In this process, the highest priority element is considered as the key feature, which will be regarded as the root node of the tree. The sequence of breadth first traveling will be recorded to be the recognition rule and saved in the symbol library. In the recognition process, the nodes with the same type as the key features can be located first in the drawing. Unnecessary matching calculations would be greatly reduced because of the accurate location. The other elements around, which satisfy the topology structure of the constrained tree, will be found next. The target symbol is recognized if all of the elements and constraints in the tree are found. Moreover, an extra preprocessing analysis approach is proposed to address repeat modes in a symbol. Thus, similar symbols can be recognized by one rule. We evaluate the proposed approach on the GREC databases and the real engineering drawings. The experimental results validate its effectiveness and efficiency.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (147 K)Download as PowerPoint slideHighlights► We extended the existing relation representation to express more symbols clearly. ► Key feature is defined to accelerate the recognition process. ► The knowledge of symbols can be acquired automatically. ► Similar symbols can be recognized with only one pattern using the virtual relation. ► Mirror and scale transformation forms can be recognized correctly.