Machine-Learning-Based Identification of Drawing Attributes

A non-transitory computer-readable medium, wherein the non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing system to:

• access a drawing associated with a construction project;
• identify, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing;
• for each candidate textual element in the set of candidate textual elements, (i) determine a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (ii) input the respective dataset for the candidate textual element into a machine-learning model that functions to (a) evaluate the respective dataset and (b) based on the evaluation, output a respective score indicating a likelihood that the candidate textual element represents the title of the drawing;
• based on the respective scores for the candidate textual elements in the set of candidate textual elements that are output by the machine-learning model, select one given candidate textual element in the set of candidate textual elements as the title of the drawing;
• and
• populate a data attribute field representing the title of the drawing with the given candidate textual element.

A method comprising:

• accessing a drawing associated with a construction project;
• identifying, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing;
• for each candidate textual element in the set of candidate textual elements, (i) determining a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (ii) inputting the respective dataset for the candidate textual element into a machine-learning model that functions to (a) evaluate the respective dataset and (b) based on the evaluation, output a respective score indicating a likelihood that the candidate textual element represents the title of the drawing;
• based on the respective scores for the candidate textual elements in the set of candidate textual elements that are output by the machine-learning model, selecting one given candidate textual element in the set of candidate textual elements as the title of the drawing;
• and
• populating a data attribute field representing the title of the drawing with the given candidate textual element.