A Robust Barcode Reading Method Based on Image Analysis of a Hierarchical Feature Classification

Xiangju Lu, Guoliang Fan, Yunkuan Wang

Abstract: Computer vision and pattern recognition technologies make robots' eyes possible, which has rapidly promoted the development of robotic sciences. Barcode, which is simple but contains much information, has been used in navigation, manufacture and other situations by robots. Reading a barcode based on image analysis is an essential technique for such a robot system. Unfortunately, few existing image analysis methods about it are capable of identifying the codes under uncertain conditions, such as varying illumination, different sizes or orientations. This paper presents a method which can locate barcode region at an uncertain 3-D background through hierarchically extracting the distinct features of it with a high precision even for the broken, aberrant, partly blotted out stripes and other poor qualities of images. The method begins with a barcode quality assessment, in which a set of omnidirectional scan lines (active set lines) and a set of description of the whole image (resembling barcode measure), are defined. According to RBM, a series of corresponding methods are chosen to have a pre-processing process of the image and decode the codes respectively. The experiment results prove that our proposed method has two main characteristics. One is fast to locate barcode (get ROI of barcode). The other is to read barcode under unpredicted conditions robustly