Locating Various Ship License Numbers in the Wild: An Effective Approach

Ship License Numbers (SLNs) play a significant role in intelligent waterway transportation systems. However, there is a dearth of published papers concerning the location of SLNs. In this paper, we present an effective coarse-to-fine approach for locating various SLNs. The proposed method contains four main steps. First, for ships in the wild, the coarse problem of locating SLNs is posed as the detection of specific text groups. In this phase, the proximity and similarity laws of Gestalt Theory are collaboratively used to create text-group hypotheses and detect the meaningful region groups that may contain text in input images. In the second step, four SLN prior features are first summarized. An SLN prior-based fine location process is then conducted to determine the locations of text candidates that actually contain characters and text, i.e., the candidate SLNs. Two connected component classification and merging algorithms are proposed based on height continuity and vertical/horizontal projection analysis. Third, a connected-component scatter-based fake-SLN elimination algorithm is developed. The accurate positions of the SLNs in the input image are obtained in this stage. Finally, partially located SLNs are compensated by a method that characterizes the color and brightness similarity features of characters in the same SLN. The proposed approach is then applied to two collected datasets. The approach is proved to be effective with an overall ship-level location accuracy of 70.38% and a SLN-level F-measure of 0.642 on 950 images with 1374 labeled SLNs.

I. Introduction

Canals, ports, harbors and waterways are becoming increasingly congested with the expanding use of ships. As a unique identity, a ship license number has been playing a significant role in ship traffic behavior control. Automatic SLN recognition is an important part of waterway intelligent transportation systems. However, there is a dearth of published papers concerning SLN recognition.