基于单程分裂与归并图像分割的集装箱号识别

Container Code Recognition Based on Single-pass Split-merge Image Segmentation

集装箱号自动识别在海关物流管理等领域有着重要的实用价值。为了快速准确的地进行集装箱号识别,提出了一种基于图像分割和区域特征分析的集装箱号字符定位和识别方法。该方法首先基于灰度相似性运用改进的单程分裂与归并算法对图像进行自适应阈值分割,同时统计各个区域的灰度、形状、边缘强度等特征;然后根据字符区域特征,利用一定规则来滤除非字符区域;最后,对于定位出的字符区域,再依据区域特征进行二值化,并采用神经网络与模板匹配相结合的方法进行识别。在包含1 804幅图像的集装箱号识别实验中,整箱号识别正确的为1 750幅,准确率为97.01%,这充分说明了算法的有效性。

Automatic container code recognition （CCR） plays an important role in custom logistics and transport management. This paper proposes an algorithm for container character location and recognition based on image segmentation and region feature analysis. First, an improved single-pass split-merge image segmentation algorithm is used to segment image based on adaptive threshold, and features of every region are obtained simultaneously. Next, character regions are separated from non-character regions by rules. Finally, model match and neural network are used to recognize every character binary region. Experimental results on 1 804 container images show that the overall accuracy can reach 97.01% （1 750 images） with the proposed algorithms.