1D Bar Code Reading on Camera Phones

The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.