TY - EJOU AU - Zhang, Xianzhe AU - Zhou, Sheng AU - Fang, Jun AU - Ni, Yanling TI - Pattern Recognition of Construction Bidding System Based on Image Processing T2 - Computer Systems Science and Engineering PY - 2020 VL - 35 IS - 4 SN - AB - Bidding for construction projects is a very important and representative field in the industry. The system of bidding for construction projects has made considerable progress over the years due to the accumulation of experience and many contributions to the field. Nowadays, with the rapid development of information technology and the intellectualization of the bidding system for construction projects, the accumulation and processing of data has become an essential element of its development. In order to manage the bidding system of construction engineering reasonably, this paper proposes a system based on image processing and pattern recognition technology, which can recognize patterns in the bidding process of construction engineering. Firstly, through binarization and other methods, the bidding project documents of construction projects are preprocessed in order to extract the main information from the documents; secondly, information is obtained through pattern recognition and processing; finally, the results of pattern recognition are imported into the bidding system, and the bidding information is quickly added to the database. This paper proposes a bidding system for construction projects based on image processing and pattern recognition. The system provides a quick and convenient means of importing the bidding information and allows personnel to efficiently process the bidding documents, particularly when there are numerous documents and and too much information. Finally, the user survey shows that the proposed bidding system based on image processing and pattern recognition can reduce the workload of construction project personnel, greatly facilitating the progress of the project. KW - architectural engineering; image processing; pattern recognition; bidding system DO - 10.32604/csse.2020.35.247