Machine recognition of hand-drawn circuit diagrams

Edwards, B. & Chandran, V. (2000) Machine recognition of hand-drawn circuit diagrams. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal, IEEE, Istanbul, Turkey, pp. 3618-3621.

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An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

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ID Code: 45580
Item Type: Conference Paper
Refereed: No
Keywords: circuit diagrams, document image processing, handwritten character recognition, image recognition, image representation, image segmentation, mathematical morphology, circuit analysis packages, circuit simulation packages, component recognition accuracy, connected representation, database, document image analysis, hand-drawn circuit diagrams, image processing, input code generation, invariant moments, machine recognition, morphological operations, node classification, node recognition accuracy, noise removal, pixel-stack, polygonal representations, scalar pixel-distribution features, scanned image, spatially varying object pixel density, straight lines, syntactic analysis, thinned lines, vector relationships
DOI: 10.1109/ICASSP.2000.860185
ISBN: 0780362934
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2000 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 06 Sep 2011 21:49
Last Modified: 08 Sep 2011 07:32

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