Recognizing Electronic Circuits to Enrich Web Documents for Electronic Simulation

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• 关键词：Graphics recognition ; Document image analysis ; Bayesian inference ; Domain knowledge ; Top down optimization ; Circuit netlist ; Symbol recognition ; Pattern recognition
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：9657
• 期：1
• 页码：60-74
• 丛书名：Graphic Recognition. Current Trends and Challenges
• ISBN：978-3-319-52159-6
• 卷排序：9657

文摘

With the objective of creating an interface for experimenting with electronic circuits embedded in documents or images, in this paper we have presented a system for parsing and understanding of electronic circuit diagrams. The developed system consists of following steps- symbol extraction, symbol recognition, optimization and netlist-representation. Firstly, symbols are extracted from the image by removing text and connection lines using computer vision techniques. For symbol recognizer a probabilistic-SVM classifier is built using HOG and radon features on training data. A Bayesian framework is used to incorporate domain knowledge information to improve the performance of the probabilistic symbol recognizer. An novel optimization approach based on top-down features is used to remove the errors that occurs in the symbol extraction and recognition task. A depth first traversal algorithm is used to find the connections between the symbols and then image is represented in the form of usable data structure. The system is evaluated on a dataset of 20 analog electronic circuit images collected from various sources and the results are presented.