International Business Machines Corporation (Armonk, NY)

A method and system to extract information from a drawing. The method involves classifying the nodes in the drawing, extracting attributes from nodes, determining whether there are any errors in the node attributes, then removing the nodes from drawing. This method includes finding edges and extracting attributes from the edges. The system includes at least one processing component as well as at least one memory part. It also contains an identification component as well as a correction component as well as an extraction component. The identification component is set to categorize nodes within the drawing, eliminate nodes from drawing, and determine edges within the drawing. The extraction component is set up to separate attributes from the edges and nodes. The correction component is designed to detect any errors in the attributes extracted.

This disclosure concerns information extraction, and more specifically information extraction from engineering plans.

An engineering drawing is a kind of technical drawing that defines the specifications for an engineered object (e.g. structural, dimensional and compositional). Also, engineering drawings can be used to describe the processes. Examples ofengineering drawings can include printed circuit board (PCB) fabrication plans, architectural drawings wiring diagrams, etc. Engineering drawings are nodes linked to each other by edges (e.g. Arrows and lines are two common examples of nodes found in engineering drawings. Annotation can be done on the edges and nodes. For instance, a node could represent a voltage source on an electrical wiring diagram. It could also be identified by a voltage value. In another example, an edge could represent an electrical wire, and is able to be tagged by the length measurement.

Numerous embodiments may be targeted towards a system with at least one processing element, at least one memory component as well as an identification component. An attribute database can be part of the system. It may contain definitions of the node symbols as well as information on domains. The identification component may be used to identify edges and to classify nodes in drawings. Nodeclassification may also be accomplished using region-based convolutional neural network (R-CNN) and is scale- and rotation-invariant. The extraction component extracts attributes from the nodes as well as edges (e.g. annotations to text). Thecorrection component detects mistakes in the extracted attributes.

Other embodiments can be directed towards a method that includes classifying nodes in a drawing and extracting attributes from the nodes. An R-CNN can be used to categorize nodes. It can also be scale-invariant. This method could be utilized to determine if there are any mistakes in the extract node attribute. It is also possible to remove nodes fromthe drawing. The method also involves the identification of edges and the extraction of attributes from the edges. Recognizing the edges could involve carrying out edge detecting as well as reinforcement learning algorithms. Extracting attributes from nodes as well as edges could involve comparing the extracted attributes to data in the database of attributes. This database can contain data from published standards. Domain information can be used to identify if there’s any mistakes in the edge and node attributes.

Additional embodiments are a computer program product that extracts data from the drawing. The computer program product includes an readable computer storage medium containing program instructions that are executable by a processor that causes a device to perform the process of the classification of nodes in the drawing, as well as taking attributes of the nodes. The node classification can be performed by using an R-CNN. This method could also be used to determine whether there are any mistakes in the extracted node attribute and remove the nodes from drawings. This method includes identifying edges, and then extracting attributes from them. Finally, it determines the presence of errors in the edgesattributes that were extracted. Identifying the edges can include the use of edge detection and reinforcement learning algorithms. The determination of whether there are mistakes in the node or edge attributes could be based on domain information stored in an attribute database.

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