Hu, JuhuaZhong, Deyang2023-08-142023-08-142023-08-142023Zhong_washington_0250O_25397.pdfhttp://hdl.handle.net/1773/50112Thesis (Master's)--University of Washington, 2023Smart detection and recognition of the driving environment are critical tasks in the automobile industry, while understanding road signs is a complicated task. When the traffic is heavy or the parking sign is unclear, drivers cannot finish street curbside parking efficiently, which blocks traffic and makes it worse. Numerous object detection and recognition techniques have been employed to address this issue, but the study for automatic street parking sign understanding, particularly street parking text recognition, is relatively limited. This work bridges the gap between scene text recognition and a smart street curbside parking system. Concretely, we propose a smart street parking sign text recognition method that utilizes a large synthetic data and one real parking sign text data. We focus on providing a multi-candidates technique built upon one general text recognition method and including specific parking sign text words in the candidates' dictionary. The former collects more text information and reduces potential errors, while the latter increases specificity and performance for the parking sign text recognition task. We compare the performance of leading text recognition engines with our proposed method in a real parking sign text data set. We show significant improvements, demonstrating the feasibility and superiority of our new proposal.application/pdfen-USnonedeep learningstreet parkingtext recognitionComputer scienceThesis