This paper presents a system f o r detecting dimension sets in engineering drawings that are drawn t o ANSI drafting standards.A new rule-based text/graphics separation algorithm and a model-based procedure for detecting arrowheads in any ofientation have been developed. Arrowhead tracking and search methods are used to extract leaders, tails, and witness lines from segmented images containing only graphics. Text blocks and feature control frames extracted from the segmented images are then associated with their corresponding leaders to obtain complete dimension sets. Object lines are then separated from centerlines and hatching lines. Experimental results are presented.

No abstract

No abstract

Classification of object lines in mechanical part drawings is a critical problem for automated conversion of drawings from paper medium to CAD databases. We describe new methods for classifying object lines. These methods include section line detection, hidden line detection, centerline detection and object line extraction. A self-supervised approach which includes a spacing estimation step and a recognition step to extract section lines is described. A general purpose algorithm which not only detects dashed lines hut also classifies them based on their attributes is described. These attributes are used for classification of detected dashed lines as hidden lines or centerlines. Object line extraction facilitates intelligent interpretation of geometric objects for integration with CAD/CAM systems.

No abstract